How does risk-taking propensity change across the life span? We contribute to answering this question using a coordinated analysis of longitudinal panels and obtaining meta-analytic estimates of age differences in risk-taking propensity across several domains. Specifically, we report results from 10 longitudinal panels (24 samples; 169845 unique respondents) covering general and domain-specific risk-taking propensity (financial, driving, recreational, occupational, health, social) across three or more waves spanning up to 28 years. The meta-analytic results revealed a negative relation between age and both general and domain-specific risk-taking propensity. Age differences, however, were more pronounced in specific domains, with age showing larger negative effects in the recreational and occupational domains. This work suggests there is need to understand the domain-specific nature of age differences in risk-taking propensity across the life span.
The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity, including general, financial, driving, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, the meta-analytic results and trajectory plots. We also tested individual predictors that are not included in the simple trajectory model in meta regression: continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert Github link)
This section offers a detailed overview of the different samples included in the analyses of the paper Age differences in risk-taking propensity: A coordinated analysis of longitudinal panels.
Each panel is described in a separate tab. We include the following:
Panel name: Full name of the panel.
Description: This is a general description of the objectives of the panel.
Country/Countries: Country or countries in which data are collected.
Waves: Waves available in the raw data set (not all waves were necessarily included in the data analysis as not every wave had collected data on the variables of interest)
Data collection period: Data collection period of the waves available in the raw data set.
Dataset(s) version number/name: Version number(s) or name(s) or raw dataset(s).
Data access: Link to directly access or request access to the raw dataset(s).
Age distribution: The density of each age and the number of observations in each age-bin(s).
Risk-taking propensity density: The raw score and standard Z-score risk-taking propensity density in every domain(s).
Panel Name: DNB Household Survey (DHS)
Description: The DNB Household Survey, undertaken by CentERdata at Tilburg University since 1993, provides annual financial information on 2,000 Dutch households. DNB Household Survey topics include: work, pensions, accommodation, mortgages, income, assets, liabilities, health, perception of personal financial situation and perception of risks.
More information at: (homepage)[link]
Country/Countries: Netherlands
Waves: 1993-2020
Data collection period: 1993-2020
Dataset(s) version number/name: NA
Data access: https://www.dhsdata.nl/site/users/login
Age distribution
Risk-taking propensity density:
Financial
Panel Name: Preference Parameters Study (GCOE) Japan Sample
Description: The Preference Parameters Study of Osaka University is an extensive panel study in 4 different countries (Japan, United States, China and India). It aims to caculate parameters of preferences defining utility function; time preference, risk aversion, habit formation, externality, as well as sociodemographic characteristics. In China and India, surveys were conducted separately in urban and rural areas.
The panel survey in Japan has been conducted annually since 2003 using a random sample drawn from men and women aged 20-69 years old by a self-administered placement method. Fresh samples were selected and added in respondents to the survey for wave 2004, 2006 and 2009.
More information at: https://www.iser.osaka-u.ac.jp/survey_data/eng_panelsummary.html
Country/Countries: Japan
Waves: 2004-2010
Data collection period: 2003-2018
Dataset(s) version number/name: NA
Data access: https://www.iser.osaka-u.ac.jp/survey_data/eng_application.html
Age distribution:
Risk-taking propensity density:
General
Panel Name: Preference Parameters Study (GCOE) USA Sample
Description: The Preference Parameters Study of Osaka University is an extensive panel study in 4 different countries (Japan, United States, China and India). It aims to caculate parameters of preferences defining utility function; time preference, risk aversion, habit formation, externality, as well as sociodemographic characteristics. In China and India, surveys were conducted separately in urban and rural areas.
The panel survey for the GCOE USA sample has been conducted annually since 2005 using a random sample drawn from men and women aged 18-99 years old by a self-administered placement method. Fresh samples were selected and added in respondents to the survey for wave 2007, 2008 and 2009.
More information at: https://www.iser.osaka-u.ac.jp/survey_data/eng_panelsummary.html
Country/Countries: United States
Waves: 2005-2010
Data collection period: 2005-2013
Dataset(s) version number/name: NA
Data access: https://www.iser.osaka-u.ac.jp/survey_data/eng_application.html
Age distribution:
Risk-taking propensity density:
General
Panel Name: Household, Income and Labour Dynamics in Australia (HILDA)
Description: The Household, Income and Labour Dynamics in Australia (HILDA) Survey is a household-based panel study that collects information about economic and personal well-being, labour market dynamics and family life of participants. Since 2001, the study has been following more than 17,000 Australian participants each year.
More information at: https://melbourneinstitute.unimelb.edu.au/hilda
Country/Countries: Australia
Waves: Wave I - Wave 19
Data collection period: 2001-present
Dataset(s) version number/name: NA
Data access: https://melbourneinstitute.unimelb.edu.au/hilda/for-data-users
Age distribution
Risk-taking propensity density:
Financial
Panel Name: Health and Retirement Study (HRS)
Description: The Health and Retirement Study (HRS) is a longitudinal panel study that surveys a representative sample of approximately 20,000 people in America. The target population for the first wave of the HRS was adults residing in households in the contiguous United States born between 1931 and 1941 (i.e., those who were between the ages of 51–61 in 1992 when the study began). One particular strength of the HRS sample design is the use of a steady-state sampling design: a new cohort of individuals age 51–56 is added every 6 years. Individuals and their spouses or partners are followed until their death. Data have been collected biannually since 1992.
More information at: https://hrs.isr.umich.edu/about
Country/Countries: United States
Waves: 2014-2018
Data collection period: 1984-present
Dataset(s) version number/name: Core Waves 1992-2018
Data access: https://hrsdata.isr.umich.edu/data-products/public-survey-data
Age distribution:
Risk-taking propensity density:
General
Driving
Financial
Recreational
Occupational
Health
Panel Name: Life in Kyrgyzstan (LIKS)
Description: The ‘Life in Kyrgyzstan’ Study is a longitudinal survey of households and individuals in Kyrgyzstan. It tracks the same 3,000 households and 8,000 individuals over time in all seven Kyrgyz regions (oblasts) and the two cities of Bishkek and Osh. The data are representative nationally and at the regional level (East, West, North, South). The survey interviews all adult household members about household demographics, assets, expenditure, migration, employment, agricultural markets, shocks, social networks, subjective well-being, and many other topics. Some of these topics are addressed in each wave while other topics are only addressed in selected waves. All members of the households in 2010 are tracked for each wave and new household members are added to the survey and tracked as well. The survey was first conducted in 2010 and it has been repeated four times in 2011, 2012, 2013 and 2016. The sixth wave of the LiK Study was conducted during November 2019-February 2020.
More information at: https://lifeinkyrgyzstan.org/about/
Country/Countries: Kyrgyzstan
Waves: 2010, 2011, 2012, 2013, 2016
Data collection period: 2010-present
Dataset(s) version number/name: NA
Data access: https://lifeinkyrgyzstan.org/data-access/
Age distribution:
Risk-taking propensity density:
General
Panel Name: Panel on Household Finances (PHF)
Description: The German Panel on Household Finances (PHF) is a panel survey on household finance and wealth in Germany, covering the balance sheet, pension, income, work life and other demographic characteristics of private households living in Germany. The first wave of the PHF was carried out in 2010/2011, the second and third wave in 2014 and 2017, respectively. In the first wave, around 3,500 randomly selected households participated, from which about 2,200 also participated in the second wave.The fourth wave is schedules to start in spring 2021.
More information at: https://www.bundesbank.de/en/bundesbank/research/panel-on-household-finances
Country/Countries: Germany
Waves: Wave 1-Wave 3
Data collection period: 2010-present
Dataset(s) version number/name: NA
Data access: https://www.bundesbank.de/en/bundesbank/research/panel-on-household-finances/data-access-and-data-protection
Age distribution:
Risk-taking propensity density:
General
Financial
Panel Name: Sparen und Altersvorsorge in Deutschland (SAVE)
Description: The Sparen und Altersvorsorge in Deutschland (SAVE) is a representative, longitudinal study on households’ financial behavior with a special focus on savings and old-age provision. Started in 2001, SAVE has collected data on households’ financial structure and relevant socio- and psychological aspects until 2013.
More information at: https://www.mpisoc.mpg.de/en/social-policy-mea/research/save-2001-2013/
Country/Countries: Germany
Waves: 2001-2013
Data collection period: 2001-2013
Dataset(s) version number/name: NA
Data access: https://dbk.gesis.org/dbksearch/GDESC2.asp?no=0014&search=save&search2=&DB=d&tab=0¬abs=&nf=1&af=&ll=10
Age distribution:
Risk-taking propensity density:
Driving
Financial
Recreational
Occupational
Health
Panel Name: German Socio-Economic Panel (SOEP)
Description: The Socio-Economic Panel (SOEP) is one of the largest and longest-running multidisciplinary household surveys worldwide. Every year, approximately 30,000 people in 15,000 households are interviewed for the SOEP study. The SOEP is also a research-driven infrastructure based at DIW Berlin. The SOEP team prepares survey data for use by researchers around the globe, and team members use the data in research on various topics. Studies based on SOEP data examine diverse aspects of societal change.
More information at: https://www.diw.de/en/diw_01.c.600489.en/about_us.html#c_624242
Country/Countries: Germany
Waves: 2004-2018
Data collection period: 1984-present
Dataset(s) version number/name: SOEP-Core v35
Data access: https://www.diw.de/sixcms/detail.php?id=diw_01.c.742256.en
Age distribution:
Risk-taking propensity density:
General
Driving
Financial
Recreational
Occupational
Health
Social
Panel Name: UK Household Longitudinal Survey (Understanding Society) (USoc)
Description: THe UK Household Longitudinal Study/Understanding Society (USoc) is built on the British Household Panel Survey (BHPS) which ran from 1991-2009 and had around 10,000 households in it. Understanding Society started in 2009 and interviewed around 40,000 households, including around 8,000 of the orginal BHPS households.The USoc examines how life in the UK is changing and what stays the same over many years and includes questions on various topics including social, economical and behavioral factors. Interviews are held with each member of the household in order to examine how different generations experience life in the UK.
More information at: https://www.understandingsociety.ac.uk/about/about-the-study
Country/Countries: United Kingdom
Waves: 2008, 2013, 2014
Data collection period: Waves 1-11, 2008-2018
Dataset(s) version number/name: Understanding Society: Innovation Panel
Data access: https://www.understandingsociety.ac.uk/documentation/access-data
Age distribution:
Risk-taking propensity density:
General
This section offers a detailed overview of the 7 different models included in the multi-level analysis in the paper Age differences in risk-taking propensity: A coordinated analysis of longitudinal panels.
Each model is described in a separate tab. We include the following:
Model name: General name of model
Description: This is a general description of the model, including some details of the model
Analysis: The code to run in R and interpret the model, along with the annotations for what each part of the code means.
Model name: Intercept only model, also called unconditional model.
Description: In the unconditional model, only the dependent variable and the grouping variable(s) (e.g., subject ID) are entered. No predictors are entered, thus the model is not “conditioned” upon any predictor variables. This intercept only model is the first step in conducting multilevel modelling, aiming to make sure mutlilevel modelling is appropriate in the first place.
Analysis: Model <- lmer (Risk ~ 1 + (1|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”.
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ 1: Specifies an unconditional model in the form DV~IV. When there are no predictors, 1 is entered in the IV’s place. In our model, Risk is the DV, representing the risk-taking propensity.
1|ID: Specifies that level-1 observations are grouped by the level-2 variable called “ID”, representing the subjects ID number.
data = DATA: Specifies that the variables (e.g., Risk, ID) are in a dataset called “DATA”.
Model name: Fixed effect model, also called age fixed-effects model.
Description: After determining that a multilevel model is appropriate, the next step is to begin to add level-1 predictors. Within multilevel modeling of real-time monitoring data, level-1 is almost always the “observation” level. In our analysis, the level one predictor is “age”. In the fixed effect model, we regard age as a predictor but did not consider differences across participants, so called fixed effect model.
Analysis: Model <- lmer (Risk ~ age + (1|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”.
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ age: Formula that lme4 will process, specified in the form DV~IV. In our model, age is not the raw age. We centered the age variable to a reference age (50 years old) and standardized the age variable to decades by dividing it by 10, then use the transformed age in our model.
1|ID: Specifies that level-1 observations are grouped by the level-2 variable called “ID”, representing the subjects ID number.
data = DATA: Specifies that the variables (e.g., Risk, ID) are in a dataset called “DATA”.
Model name: Linear model, also called age fixed and random effects model
Description: In the linear model, we regard age as a predictor and also include differences across participants, so in turn, this model included age both as a fixed and a random slope.
Analysis: Model <- lmer (Risk ~ age + (1+age|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ age: Formula that lme4 will process, specified in the form DV~IV, the independent variable in the model is centered and standardized age.
1+age|ID: Specifies that the model include not only age fixed effect, but also age random effect.
data = DATA: Specifies that the variables (e.g., Risk, age, ID) are in a dataset called “DATA”
Model name: Linear with gender model, also called age fixed and random effects model with gender
Description: The next step involves entering level-2 effects, although it is not always necessary to take this piecewise approach testing a level-1-effects-only model first. A model with level-2 variables should only be used when the theoretical conceptualization of the model necessitates it and there is sufficient power to do so. In this model, we are interested in adjusting for the effect of gender, so enter gender as a level-2 predictor. In this way, we coded the relation between inter-individual differences in the change trajectories and the time-invariant characteristic (gender) of the individual to compare whether age is associated with risk-taking propensity in males and females in same manner.
Analysis: Model <- lmer (Risk ~ age + gender + (1+age|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”.
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ age + gender: Formula that lme4 will process, specified in the form DV~IV1+IV2, the first independent variable in the model is level-1 predictor (i.e., centered and standardized age), and the second independent variable in the model is level-2 predictor(i.e.,gender).
1+age|ID: Specifies that the model include not only age fixed effect, but also age random effect.
data = DATA: Specifies that the variables (e.g., Risk, age, gender, ID) are in a dataset called “DATA”
Model name: Linear with gender interaction model, also called age fixed and random effects model with gender, including an age by gender interaction
Description: This model further included an age by gender interaction based on previous model.
Analysis: Model <- lmer (Risk ~ age + age\(\times\)gender + (1+age|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ age + age\(\times\)gender: Formula that lme4 will process, specified in the form DV~IV1+IV2, the first independent variable in the model is level-1 predictor (i.e., centered and standardized age), and the second independent variable in the model is the interaction between age and gender.
1+age|ID: Specifies that the model include not only age fixed effect, but also age random effect.
data = DATA: Specifies that the variables (e.g., Risk, age, gender, ID) are in a dataset called “DATA”
Model name: Quadratic model, also called age quadratic growth model
Description: we fit quadratic growth models to assess non-linear change. We did this by squaring age variable and entering this into a model.
Analysis: Model <- lmer (Risk ~ age + I(\(Age^2\))) + (1+age|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ age + I(\(age^2\)): Formula that lme4 will process, specified in the form DV~IV1+IV2, the first independent variable in the model is level-1 predictor (i.e., centered and standardized age), and the second independent variable in the model is quadratic age.
1+age|ID: Specifies that the model include not only age fixed effect, but also age random effect.
data = DATA: Specifies that the variables (e.g., Risk, ID) are in a dataset called “DATA”
Model name: Quadratic with gender model, also called age quadratic growth model with gender.
Description: We added gender variable into quadratic growth model to assess potential age differences in the quadratic trajectories.
Analysis: Model <- lmer (Risk ~ age + I(\(age^2\)) + gender + (1+age|ID), data = DATA)
Model: Tells R to save the output of the analyses to an object called “Model”
lmer: This is the command to test a mixed linear model using lme4.
Risk ~ age + I(\(age^2\)) + gender: Formula that lme4 will process, specified in the form DV~IV1+IV2+IV3, the first independent variable in the model is level-1 predictor (i.e., centered and standardized age), the second independent variable in the model is quadratic age, and the third independent variable in the model is level-2 predictor(i.e.,gender).
1+age|ID: Specifies that the model include not only age fixed effect, but also age random effect.
data = DATA: Specifies that the variables (e.g., Risk, ID) are in a dataset called “DATA”
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Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.1342 -8.2685 -4.2685 -4.6849 -0.2685
##
## tau^2 (estimated amount of total heterogeneity): 0.0147 (SE = 0.0086)
## tau (square root of estimated tau^2 value): 0.1213
## I^2 (total heterogeneity / total variability): 99.82%
## H^2 (total variability / sampling variability): 553.04
##
## Test for Heterogeneity:
## Q(df = 6) = 3786.0223, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4346 0.0460 9.4423 <.0001 0.3443 0.5248 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0198 -6.0396 1.9604 -0.4945 41.9604
##
## tau^2 (estimated amount of residual heterogeneity): 0.0129 (SE = 0.0092)
## tau (square root of estimated tau^2 value): 0.1135
## I^2 (residual heterogeneity / unaccounted variability): 99.69%
## H^2 (unaccounted variability / sampling variability): 319.04
## R^2 (amount of heterogeneity accounted for): 12.51%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1279.3991, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.8425, p-val = 0.2414
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3294 0.0804 4.0984 <.0001 0.1719 0.4869 ***
## continentEurope 0.1753 0.1040 1.6859 0.0918 -0.0285 0.3791 .
## continentNorth America 0.1063 0.1137 0.9347 0.3500 -0.1166 0.3291
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.8526 -7.7053 -1.7053 -2.8770 22.2947
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0080)
## tau (square root of estimated tau^2 value): 0.1118
## I^2 (residual heterogeneity / unaccounted variability): 99.77%
## H^2 (unaccounted variability / sampling variability): 437.61
## R^2 (amount of heterogeneity accounted for): 15.07%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 3633.8072, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0623, p-val = 0.1510
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0036 0.3031 0.0119 0.9905 -0.5904 0.5976
## mean.age 0.0083 0.0058 1.4361 0.1510 -0.0030 0.0195
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.1342 -8.2685 -4.2685 -4.6849 -0.2685
##
## tau^2 (estimated amount of total heterogeneity): 0.0147 (SE = 0.0086)
## tau (square root of estimated tau^2 value): 0.1213
## I^2 (total heterogeneity / total variability): 99.82%
## H^2 (total variability / sampling variability): 553.04
##
## Test for Heterogeneity:
## Q(df = 6) = 3786.0223, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4346 0.0460 9.4423 <.0001 0.3443 0.5248 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2549 -22.5098 -18.5098 -18.9262 -14.5098
##
## tau^2 (estimated amount of total heterogeneity): 0.0011 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0337
## I^2 (total heterogeneity / total variability): 97.86%
## H^2 (total variability / sampling variability): 46.68
##
## Test for Heterogeneity:
## Q(df = 6) = 211.1282, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0774 0.0132 -5.8788 <.0001 -0.1032 -0.0516 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2056 -14.4112 -6.4112 -8.8660 33.5888
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0372
## I^2 (residual heterogeneity / unaccounted variability): 97.23%
## H^2 (unaccounted variability / sampling variability): 36.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 109.2884, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.3748, p-val = 0.5029
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0823 0.0268 -3.0672 0.0022 -0.1349 -0.0297 **
## continentEurope -0.0105 0.0350 -0.3002 0.7640 -0.0792 0.0581
## continentNorth America 0.0298 0.0378 0.7879 0.4308 -0.0443 0.1038
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.0335 -18.0670 -12.0670 -13.2387 11.9330
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (residual heterogeneity / unaccounted variability): 98.19%
## H^2 (unaccounted variability / sampling variability): 55.18
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 187.4077, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3759, p-val = 0.5398
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1381 0.0995 -1.3882 0.1651 -0.3331 0.0569
## mean.age 0.0012 0.0019 0.6131 0.5398 -0.0025 0.0049
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2549 -22.5098 -18.5098 -18.9262 -14.5098
##
## tau^2 (estimated amount of total heterogeneity): 0.0011 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0337
## I^2 (total heterogeneity / total variability): 97.86%
## H^2 (total variability / sampling variability): 46.68
##
## Test for Heterogeneity:
## Q(df = 6) = 211.1282, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0774 0.0132 -5.8788 <.0001 -0.1032 -0.0516 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2551 -22.5101 -18.5101 -18.9266 -14.5101
##
## tau^2 (estimated amount of total heterogeneity): 0.0011 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0336
## I^2 (total heterogeneity / total variability): 97.80%
## H^2 (total variability / sampling variability): 45.56
##
## Test for Heterogeneity:
## Q(df = 6) = 193.2894, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0132 -5.8895 <.0001 -0.1033 -0.0517 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2040 -14.4081 -6.4081 -8.8629 33.5919
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0371
## I^2 (residual heterogeneity / unaccounted variability): 97.18%
## H^2 (unaccounted variability / sampling variability): 35.41
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 103.4725, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.3757, p-val = 0.5026
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0833 0.0268 -3.1083 0.0019 -0.1358 -0.0308 **
## continentEurope -0.0091 0.0350 -0.2595 0.7953 -0.0776 0.0595
## continentNorth America 0.0309 0.0377 0.8187 0.4129 -0.0431 0.1049
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.0533 -18.1066 -12.1066 -13.2782 11.8934
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0361
## I^2 (residual heterogeneity / unaccounted variability): 98.12%
## H^2 (unaccounted variability / sampling variability): 53.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 168.7653, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4205, p-val = 0.5167
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1413 0.0989 -1.4284 0.1532 -0.3351 0.0526
## mean.age 0.0012 0.0019 0.6485 0.5167 -0.0025 0.0049
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2551 -22.5101 -18.5101 -18.9266 -14.5101
##
## tau^2 (estimated amount of total heterogeneity): 0.0011 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0336
## I^2 (total heterogeneity / total variability): 97.80%
## H^2 (total variability / sampling variability): 45.56
##
## Test for Heterogeneity:
## Q(df = 6) = 193.2894, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0132 -5.8895 <.0001 -0.1033 -0.0517 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.9379 -21.8758 -17.8758 -18.2923 -13.8758
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0360
## I^2 (total heterogeneity / total variability): 98.13%
## H^2 (total variability / sampling variability): 53.45
##
## Test for Heterogeneity:
## Q(df = 6) = 204.6185, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0787 0.0140 -5.6151 <.0001 -0.1061 -0.0512 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.5604 -9.1208 -5.1208 -5.5372 -1.1208
##
## tau^2 (estimated amount of total heterogeneity): 0.0123 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (total heterogeneity / total variability): 98.17%
## H^2 (total variability / sampling variability): 54.61
##
## Test for Heterogeneity:
## Q(df = 6) = 281.8081, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2448 0.0433 -5.6555 <.0001 -0.3297 -0.1600 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.0462 -14.0925 -6.0925 -8.5473 33.9075
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (residual heterogeneity / unaccounted variability): 97.46%
## H^2 (unaccounted variability / sampling variability): 39.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 107.0571, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4893, p-val = 0.4749
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0870 0.0280 -3.1044 0.0019 -0.1419 -0.0321 **
## continentEurope -0.0065 0.0365 -0.1778 0.8589 -0.0781 0.0651
## continentNorth America 0.0362 0.0395 0.9164 0.3595 -0.0412 0.1136
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.1603 -6.3207 1.6793 -0.7755 41.6793
##
## tau^2 (estimated amount of residual heterogeneity): 0.0115 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.1071
## I^2 (residual heterogeneity / unaccounted variability): 96.89%
## H^2 (unaccounted variability / sampling variability): 32.18
## R^2 (amount of heterogeneity accounted for): 6.71%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 171.4505, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.3606, p-val = 0.3072
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3408 0.0771 -4.4186 <.0001 -0.4920 -0.1896
## continentEurope 0.1192 0.1014 1.1754 0.2398 -0.0795 0.3178
## continentNorth America 0.1595 0.1087 1.4676 0.1422 -0.0535 0.3725
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.7644 -17.5288 -11.5288 -12.7005 12.4712
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (residual heterogeneity / unaccounted variability): 98.42%
## H^2 (unaccounted variability / sampling variability): 63.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 183.3979, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3432, p-val = 0.5580
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1406 0.1062 -1.3235 0.1857 -0.3488 0.0676
## mean.age 0.0012 0.0020 0.5859 0.5580 -0.0028 0.0051
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7540 -7.5081 -1.5081 -2.6797 22.4919
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0085)
## tau (square root of estimated tau^2 value): 0.1118
## I^2 (residual heterogeneity / unaccounted variability): 97.99%
## H^2 (unaccounted variability / sampling variability): 49.76
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 201.6124, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9182, p-val = 0.3379
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.5341 0.3050 -1.7510 0.0799 -1.1319 0.0637 .
## mean.age 0.0055 0.0058 0.9582 0.3379 -0.0058 0.0169
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.9379 -21.8758 -17.8758 -18.2923 -13.8758
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0360
## I^2 (total heterogeneity / total variability): 98.13%
## H^2 (total variability / sampling variability): 53.45
##
## Test for Heterogeneity:
## Q(df = 6) = 204.6185, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0787 0.0140 -5.6151 <.0001 -0.1061 -0.0512 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.5604 -9.1208 -5.1208 -5.5372 -1.1208
##
## tau^2 (estimated amount of total heterogeneity): 0.0123 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (total heterogeneity / total variability): 98.17%
## H^2 (total variability / sampling variability): 54.61
##
## Test for Heterogeneity:
## Q(df = 6) = 281.8081, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2448 0.0433 -5.6555 <.0001 -0.3297 -0.1600 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.2162 -20.4325 -16.4325 -16.8489 -12.4325
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0372
## I^2 (total heterogeneity / total variability): 96.30%
## H^2 (total variability / sampling variability): 27.05
##
## Test for Heterogeneity:
## Q(df = 6) = 89.5472, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0818 0.0148 -5.5115 <.0001 -0.1109 -0.0527 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.5701 -9.1401 -5.1401 -5.5566 -1.1401
##
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0075)
## tau (square root of estimated tau^2 value): 0.1104
## I^2 (total heterogeneity / total variability): 97.52%
## H^2 (total variability / sampling variability): 40.25
##
## Test for Heterogeneity:
## Q(df = 6) = 249.0043, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2377 0.0432 -5.4964 <.0001 -0.3224 -0.1529 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9404 -27.8808 -23.8808 -24.2973 -19.8808
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0183
## I^2 (total heterogeneity / total variability): 77.22%
## H^2 (total variability / sampling variability): 4.39
##
## Test for Heterogeneity:
## Q(df = 6) = 27.3247, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0077 0.0086 0.8976 0.3694 -0.0092 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.2912 -12.5825 -4.5825 -7.0373 35.4175
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0444
## I^2 (residual heterogeneity / unaccounted variability): 95.96%
## H^2 (unaccounted variability / sampling variability): 24.76
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 62.5198, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9938, p-val = 0.6084
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0940 0.0323 -2.9083 0.0036 -0.1574 -0.0307 **
## continentEurope -0.0006 0.0424 -0.0141 0.9888 -0.0837 0.0825
## continentNorth America 0.0378 0.0455 0.8300 0.4066 -0.0514 0.1269
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0147 -6.0294 1.9706 -0.4842 41.9706
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0094)
## tau (square root of estimated tau^2 value): 0.1111
## I^2 (residual heterogeneity / unaccounted variability): 96.34%
## H^2 (unaccounted variability / sampling variability): 27.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 167.3626, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.8843, p-val = 0.3898
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3245 0.0800 -4.0571 <.0001 -0.4813 -0.1678
## continentEurope 0.1031 0.1050 0.9820 0.3261 -0.1027 0.3089
## continentNorth America 0.1512 0.1131 1.3371 0.1812 -0.0705 0.3730
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0183
## I^2 (residual heterogeneity / unaccounted variability): 67.71%
## H^2 (unaccounted variability / sampling variability): 3.10
## R^2 (amount of heterogeneity accounted for): 0.71%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 10.8315, p-val = 0.0285
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.1442, p-val = 0.5643
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0188 0.0161 1.1709 0.2417 -0.0127 0.0503
## continentEurope -0.0218 0.0211 -1.0336 0.3013 -0.0630 0.0195
## continentNorth America -0.0076 0.0222 -0.3417 0.7326 -0.0511 0.0360
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.4037 -16.8073 -10.8073 -11.9790 13.1927
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0370
## I^2 (residual heterogeneity / unaccounted variability): 96.37%
## H^2 (unaccounted variability / sampling variability): 27.51
## R^2 (amount of heterogeneity accounted for): 0.77%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 61.0062, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0784, p-val = 0.2991
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1874 0.1028 -1.8237 0.0682 -0.3888 0.0140 .
## mean.age 0.0020 0.0020 1.0385 0.2991 -0.0018 0.0059
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7040 -7.4080 -1.4080 -2.5797 22.5920
##
## tau^2 (estimated amount of residual heterogeneity): 0.0127 (SE = 0.0086)
## tau (square root of estimated tau^2 value): 0.1127
## I^2 (residual heterogeneity / unaccounted variability): 97.68%
## H^2 (unaccounted variability / sampling variability): 43.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 207.0785, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7662, p-val = 0.3814
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.5064 0.3102 -1.6328 0.1025 -1.1143 0.1015
## mean.age 0.0052 0.0059 0.8753 0.3814 -0.0064 0.0167
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0149
## I^2 (residual heterogeneity / unaccounted variability): 69.36%
## H^2 (unaccounted variability / sampling variability): 3.26
## R^2 (amount of heterogeneity accounted for): 34.39%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 18.4986, p-val = 0.0024
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8448, p-val = 0.0917
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0903 0.0495 1.8238 0.0682 -0.0067 0.1872 .
## mean.age -0.0016 0.0010 -1.6867 0.0917 -0.0035 0.0003 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.2162 -20.4325 -16.4325 -16.8489 -12.4325
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0372
## I^2 (total heterogeneity / total variability): 96.30%
## H^2 (total variability / sampling variability): 27.05
##
## Test for Heterogeneity:
## Q(df = 6) = 89.5472, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0818 0.0148 -5.5115 <.0001 -0.1109 -0.0527 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.5701 -9.1401 -5.1401 -5.5566 -1.1401
##
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0075)
## tau (square root of estimated tau^2 value): 0.1104
## I^2 (total heterogeneity / total variability): 97.52%
## H^2 (total variability / sampling variability): 40.25
##
## Test for Heterogeneity:
## Q(df = 6) = 249.0043, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2377 0.0432 -5.4964 <.0001 -0.3224 -0.1529 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9404 -27.8808 -23.8808 -24.2973 -19.8808
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0183
## I^2 (total heterogeneity / total variability): 77.22%
## H^2 (total variability / sampling variability): 4.39
##
## Test for Heterogeneity:
## Q(df = 6) = 27.3247, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0077 0.0086 0.8976 0.3694 -0.0092 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.1022 -14.2044 -10.2044 -12.0072 1.7956
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0220
## I^2 (total heterogeneity / total variability): 95.93%
## H^2 (total variability / sampling variability): 24.57
##
## Test for Heterogeneity:
## Q(df = 3) = 68.5431, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0830 0.0114 -7.2637 <.0001 -0.1054 -0.0606 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6936 -17.3872 -13.3872 -15.1900 -1.3872
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0130
## I^2 (total heterogeneity / total variability): 96.91%
## H^2 (total variability / sampling variability): 32.34
##
## Test for Heterogeneity:
## Q(df = 3) = 142.2153, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0016 0.0068 -0.2309 0.8174 -0.0148 0.0117
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9111 -7.8223 0.1777 -7.8223 40.1777
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0475, p-val = 0.8275
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 68.4957, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0934 0.0043 -21.9754 <.0001 -0.1018 -0.0851
## continentEurope -0.0038 0.0047 -0.8022 0.4224 -0.0130 0.0054
## continentNorth America 0.0439 0.0069 6.3812 <.0001 0.0304 0.0574
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5724 -5.1448 2.8552 -5.1448 42.8552
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0178
## I^2 (residual heterogeneity / unaccounted variability): 92.59%
## H^2 (unaccounted variability / sampling variability): 13.49
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.4909, p-val = 0.0002
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5900, p-val = 0.7445
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0073 0.0130 -0.5634 0.5732 -0.0329 0.0182
## continentEurope 0.0169 0.0221 0.7681 0.4424 -0.0263 0.0602
## continentNorth America 0.0059 0.0222 0.2635 0.7921 -0.0377 0.0494
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.3925 -8.7850 -2.7850 -6.7055 21.2150
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0263
## I^2 (residual heterogeneity / unaccounted variability): 96.71%
## H^2 (unaccounted variability / sampling variability): 30.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 68.0995, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1147, p-val = 0.7348
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1390 0.1659 -0.8380 0.4021 -0.4641 0.1861
## mean.age 0.0012 0.0035 0.3387 0.7348 -0.0056 0.0080
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.0764 -14.1527 -8.1527 -12.0733 15.8473
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0065
## I^2 (residual heterogeneity / unaccounted variability): 85.54%
## H^2 (unaccounted variability / sampling variability): 6.92
## R^2 (amount of heterogeneity accounted for): 75.39%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 18.7796, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.8187, p-val = 0.0030
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1343 0.0447 -3.0028 0.0027 -0.2220 -0.0466 **
## mean.age 0.0028 0.0009 2.9696 0.0030 0.0010 0.0046 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.1022 -14.2044 -10.2044 -12.0072 1.7956
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0220
## I^2 (total heterogeneity / total variability): 95.93%
## H^2 (total variability / sampling variability): 24.57
##
## Test for Heterogeneity:
## Q(df = 3) = 68.5431, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0830 0.0114 -7.2637 <.0001 -0.1054 -0.0606 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6936 -17.3872 -13.3872 -15.1900 -1.3872
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0130
## I^2 (total heterogeneity / total variability): 96.91%
## H^2 (total variability / sampling variability): 32.34
##
## Test for Heterogeneity:
## Q(df = 3) = 142.2153, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0016 0.0068 -0.2309 0.8174 -0.0148 0.0117
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.8102 -13.6204 -9.6204 -11.4232 2.3796
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0243
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.39
##
## Test for Heterogeneity:
## Q(df = 3) = 75.6840, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0839 0.0125 -6.7163 <.0001 -0.1084 -0.0594 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8821 -17.7642 -13.7642 -15.5669 -1.7642
##
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0122
## I^2 (total heterogeneity / total variability): 96.60%
## H^2 (total variability / sampling variability): 29.45
##
## Test for Heterogeneity:
## Q(df = 3) = 132.8461, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0018 0.0064 -0.2760 0.7825 -0.0142 0.0107
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8629 -5.7259 -1.7259 -3.5286 10.2741
##
## tau^2 (estimated amount of total heterogeneity): 0.0083 (SE = 0.0071)
## tau (square root of estimated tau^2 value): 0.0913
## I^2 (total heterogeneity / total variability): 97.70%
## H^2 (total variability / sampling variability): 43.56
##
## Test for Heterogeneity:
## Q(df = 3) = 90.8738, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2923 0.0465 -6.2893 <.0001 -0.3834 -0.2012 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5394 -7.0787 0.9213 -7.0787 40.9213
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8287, p-val = 0.3626
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 74.8553, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0932 0.0042 -22.3521 <.0001 -0.1014 -0.0850
## continentEurope -0.0036 0.0046 -0.7858 0.4320 -0.0127 0.0054
## continentNorth America 0.0460 0.0068 6.7433 <.0001 0.0326 0.0594
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6567 -5.3133 2.6867 -5.3133 42.6867
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0162
## I^2 (residual heterogeneity / unaccounted variability): 91.53%
## H^2 (unaccounted variability / sampling variability): 11.81
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.8068, p-val = 0.0006
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.6639, p-val = 0.7175
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0075 0.0120 -0.6233 0.5331 -0.0310 0.0160
## continentEurope 0.0165 0.0202 0.8148 0.4152 -0.0231 0.0561
## continentNorth America 0.0059 0.0204 0.2884 0.7731 -0.0341 0.0458
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6582 -5.3163 2.6837 -5.3163 42.6837
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.5905, p-val = 0.4422
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 90.2832, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3330 0.0119 -28.1028 <.0001 -0.3563 -0.3098
## continentEurope -0.0039 0.0138 -0.2809 0.7788 -0.0310 0.0233
## continentNorth America 0.1801 0.0218 8.2727 <.0001 0.1374 0.2227
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.1403 -8.2805 -2.2805 -6.2011 21.7195
##
## tau^2 (estimated amount of residual heterogeneity): 0.0009 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0299
## I^2 (residual heterogeneity / unaccounted variability): 97.48%
## H^2 (unaccounted variability / sampling variability): 39.61
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 75.6829, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0081, p-val = 0.9283
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1008 0.1873 -0.5382 0.5905 -0.4679 0.2663
## mean.age 0.0004 0.0039 0.0900 0.9283 -0.0073 0.0080
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.1335 -14.2671 -8.2671 -12.1877 15.7329
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0062
## I^2 (residual heterogeneity / unaccounted variability): 84.88%
## H^2 (unaccounted variability / sampling variability): 6.61
## R^2 (amount of heterogeneity accounted for): 73.88%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 17.6349, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.2015, p-val = 0.0042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1258 0.0434 -2.9016 0.0037 -0.2108 -0.0408 **
## mean.age 0.0026 0.0009 2.8638 0.0042 0.0008 0.0044 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5165 -3.0330 2.9670 -0.9536 26.9670
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0128)
## tau (square root of estimated tau^2 value): 0.1117
## I^2 (residual heterogeneity / unaccounted variability): 98.15%
## H^2 (unaccounted variability / sampling variability): 53.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 90.1656, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0303, p-val = 0.8618
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4127 0.6938 -0.5948 0.5519 -1.7725 0.9471
## mean.age 0.0025 0.0145 0.1741 0.8618 -0.0259 0.0309
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.8102 -13.6204 -9.6204 -11.4232 2.3796
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0243
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.39
##
## Test for Heterogeneity:
## Q(df = 3) = 75.6840, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0839 0.0125 -6.7163 <.0001 -0.1084 -0.0594 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8821 -17.7642 -13.7642 -15.5669 -1.7642
##
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0122
## I^2 (total heterogeneity / total variability): 96.60%
## H^2 (total variability / sampling variability): 29.45
##
## Test for Heterogeneity:
## Q(df = 3) = 132.8461, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0018 0.0064 -0.2760 0.7825 -0.0142 0.0107
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8629 -5.7259 -1.7259 -3.5286 10.2741
##
## tau^2 (estimated amount of total heterogeneity): 0.0083 (SE = 0.0071)
## tau (square root of estimated tau^2 value): 0.0913
## I^2 (total heterogeneity / total variability): 97.70%
## H^2 (total variability / sampling variability): 43.56
##
## Test for Heterogeneity:
## Q(df = 3) = 90.8738, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2923 0.0465 -6.2893 <.0001 -0.3834 -0.2012 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.2825 -28.5650 -24.5650 -22.6761 -23.8150
##
## tau^2 (estimated amount of total heterogeneity): 0.0124 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1114
## I^2 (total heterogeneity / total variability): 99.33%
## H^2 (total variability / sampling variability): 150.14
##
## Test for Heterogeneity:
## Q(df = 19) = 2151.1582, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3621 0.0255 14.2162 <.0001 0.3122 0.4120 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.9787 -23.9574 -13.9574 -10.0945 -7.9574
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.1119
## I^2 (residual heterogeneity / unaccounted variability): 98.92%
## H^2 (unaccounted variability / sampling variability): 92.72
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 2095.7267, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.8559, p-val = 0.4144
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2601 0.1210 2.1502 0.0315 0.0230 0.4973 *
## continentEurope 0.0935 0.1241 0.7533 0.4513 -0.1498 0.3368
## continentNorth America 0.2022 0.1652 1.2242 0.2209 -0.1215 0.5259
## continentOceania 0.2275 0.1648 1.3802 0.1675 -0.0956 0.5505
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 17.3796 -34.7592 -28.7592 -26.0881 -27.0449
##
## tau^2 (estimated amount of residual heterogeneity): 0.0080 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0892
## I^2 (residual heterogeneity / unaccounted variability): 98.94%
## H^2 (unaccounted variability / sampling variability): 93.97
## R^2 (amount of heterogeneity accounted for): 35.86%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1793.9801, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.0852, p-val = 0.0009
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0411 0.2045 5.0899 <.0001 0.6402 1.4420 ***
## mean.age -0.0109 0.0033 -3.3294 0.0009 -0.0174 -0.0045 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.5689 -29.1378 -23.1378 -20.4666 -21.4235
##
## tau^2 (estimated amount of residual heterogeneity): 0.0110 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.1049
## I^2 (residual heterogeneity / unaccounted variability): 99.03%
## H^2 (unaccounted variability / sampling variability): 103.14
## R^2 (amount of heterogeneity accounted for): 11.29%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 2136.8043, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.3565, p-val = 0.0669
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2627 0.0595 4.4127 <.0001 0.1460 0.3794 ***
## scale2 0.0198 0.0108 1.8321 0.0669 -0.0014 0.0409 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.1293 -64.2586 -60.2586 -58.3697 -59.5086
##
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0408
## I^2 (total heterogeneity / total variability): 96.40%
## H^2 (total variability / sampling variability): 27.77
##
## Test for Heterogeneity:
## Q(df = 19) = 336.6894, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1135 0.0098 -11.5689 <.0001 -0.1327 -0.0943 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.6547 -53.3094 -43.3094 -39.4465 -37.3094
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0418
## I^2 (residual heterogeneity / unaccounted variability): 95.04%
## H^2 (unaccounted variability / sampling variability): 20.16
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 170.5200, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0403, p-val = 0.5641
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1310 0.0526 -2.4893 0.0128 -0.2342 -0.0279 *
## continentEurope 0.0146 0.0537 0.2714 0.7861 -0.0907 0.1199
## continentNorth America 0.0138 0.0686 0.2005 0.8411 -0.1207 0.1482
## continentOceania 0.0744 0.0672 1.1066 0.2684 -0.0574 0.2062
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.8263 -65.6527 -59.6527 -56.9816 -57.9384
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0335
## I^2 (residual heterogeneity / unaccounted variability): 94.38%
## H^2 (unaccounted variability / sampling variability): 17.80
## R^2 (amount of heterogeneity accounted for): 32.79%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 172.9535, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.3879, p-val = 0.0066
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1003 0.0788 1.2733 0.2029 -0.0541 0.2547
## mean.age -0.0035 0.0013 -2.7181 0.0066 -0.0059 -0.0010 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.4687 -60.9373 -54.9373 -52.2662 -53.2230
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0407
## I^2 (residual heterogeneity / unaccounted variability): 95.18%
## H^2 (unaccounted variability / sampling variability): 20.73
## R^2 (amount of heterogeneity accounted for): 0.64%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 320.1204, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1692, p-val = 0.2796
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1373 0.0241 -5.7004 <.0001 -0.1845 -0.0901 ***
## scale2 0.0047 0.0043 1.0813 0.2796 -0.0038 0.0131
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.8412 -63.6824 -59.6824 -57.7935 -58.9324
##
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0419
## I^2 (total heterogeneity / total variability): 96.20%
## H^2 (total variability / sampling variability): 26.34
##
## Test for Heterogeneity:
## Q(df = 19) = 314.2473, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1136 0.0100 -11.3631 <.0001 -0.1332 -0.0940 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.3552 -52.7104 -42.7104 -38.8475 -36.7104
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0431
## I^2 (residual heterogeneity / unaccounted variability): 95.14%
## H^2 (unaccounted variability / sampling variability): 20.56
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 179.7138, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.8958, p-val = 0.5943
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1350 0.0530 -2.5484 0.0108 -0.2388 -0.0312 *
## continentEurope 0.0188 0.0541 0.3469 0.7286 -0.0873 0.1248
## continentNorth America 0.0172 0.0697 0.2471 0.8048 -0.1193 0.1537
## continentOceania 0.0771 0.0683 1.1290 0.2589 -0.0568 0.2111
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.0865 -64.1730 -58.1730 -55.5019 -56.4588
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0361
## I^2 (residual heterogeneity / unaccounted variability): 94.60%
## H^2 (unaccounted variability / sampling variability): 18.51
## R^2 (amount of heterogeneity accounted for): 25.79%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 183.5664, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8695, p-val = 0.0154
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0905 0.0845 1.0710 0.2842 -0.0751 0.2560
## mean.age -0.0033 0.0014 -2.4227 0.0154 -0.0060 -0.0006 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.1447 -60.2894 -54.2894 -51.6183 -52.5751
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0419
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability): 20.26
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 306.4962, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0453, p-val = 0.3066
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1367 0.0247 -5.5433 <.0001 -0.1850 -0.0883 ***
## scale2 0.0045 0.0044 1.0224 0.3066 -0.0041 0.0132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.6573 -65.3147 -61.3147 -59.4258 -60.5647
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0400
## I^2 (total heterogeneity / total variability): 95.96%
## H^2 (total variability / sampling variability): 24.78
##
## Test for Heterogeneity:
## Q(df = 19) = 305.8071, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1147 0.0096 -11.9602 <.0001 -0.1335 -0.0959 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.5270 -29.0539 -25.0539 -23.1651 -24.3039
##
## tau^2 (estimated amount of total heterogeneity): 0.0117 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.1081
## I^2 (total heterogeneity / total variability): 95.93%
## H^2 (total variability / sampling variability): 24.56
##
## Test for Heterogeneity:
## Q(df = 19) = 703.1953, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2767 0.0252 -10.9722 <.0001 -0.3261 -0.2272 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.1772 -54.3544 -44.3544 -40.4914 -38.3544
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0408
## I^2 (residual heterogeneity / unaccounted variability): 94.76%
## H^2 (unaccounted variability / sampling variability): 19.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 163.3152, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.1908, p-val = 0.5338
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1402 0.0509 -2.7568 0.0058 -0.2399 -0.0405 **
## continentEurope 0.0235 0.0519 0.4521 0.6512 -0.0783 0.1253
## continentNorth America 0.0154 0.0666 0.2309 0.8174 -0.1151 0.1458
## continentOceania 0.0810 0.0652 1.2419 0.2143 -0.0468 0.2089
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.5505 -25.1010 -15.1010 -11.2381 -9.1010
##
## tau^2 (estimated amount of residual heterogeneity): 0.0111 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.1052
## I^2 (residual heterogeneity / unaccounted variability): 95.18%
## H^2 (unaccounted variability / sampling variability): 20.75
## R^2 (amount of heterogeneity accounted for): 5.38%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 466.4946, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.7882, p-val = 0.2853
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2797 0.1172 -2.3862 0.0170 -0.5095 -0.0500 *
## continentEurope -0.0002 0.1202 -0.0016 0.9987 -0.2358 0.2354
## continentNorth America -0.1155 0.1595 -0.7242 0.4689 -0.4282 0.1971
## continentOceania 0.1726 0.1578 1.0933 0.2743 -0.1368 0.4819
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 33.1384 -66.2767 -60.2767 -57.6056 -58.5624
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0340
## I^2 (residual heterogeneity / unaccounted variability): 94.12%
## H^2 (unaccounted variability / sampling variability): 17.02
## R^2 (amount of heterogeneity accounted for): 27.81%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 185.9638, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.5715, p-val = 0.0104
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0896 0.0798 1.1221 0.2618 -0.0669 0.2461
## mean.age -0.0033 0.0013 -2.5635 0.0104 -0.0058 -0.0008 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.5645 -27.1290 -21.1290 -18.4579 -19.4147
##
## tau^2 (estimated amount of residual heterogeneity): 0.0119 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.1090
## I^2 (residual heterogeneity / unaccounted variability): 95.67%
## H^2 (unaccounted variability / sampling variability): 23.07
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 671.6169, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6212, p-val = 0.4306
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4745 0.2522 -1.8814 0.0599 -0.9688 0.0198 .
## mean.age 0.0032 0.0040 0.7882 0.4306 -0.0047 0.0111
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.7734 -61.5469 -55.5469 -52.8757 -53.8326
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0405
## I^2 (residual heterogeneity / unaccounted variability): 94.85%
## H^2 (unaccounted variability / sampling variability): 19.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 295.7499, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7416, p-val = 0.3892
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1335 0.0239 -5.5958 <.0001 -0.1802 -0.0867 ***
## scale2 0.0037 0.0043 0.8612 0.3892 -0.0047 0.0121
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 15.6365 -31.2729 -25.2729 -22.6018 -23.5586
##
## tau^2 (estimated amount of residual heterogeneity): 0.0089 (SE = 0.0033)
## tau (square root of estimated tau^2 value): 0.0945
## I^2 (residual heterogeneity / unaccounted variability): 94.26%
## H^2 (unaccounted variability / sampling variability): 17.41
## R^2 (amount of heterogeneity accounted for): 23.61%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 342.5383, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.6809, p-val = 0.0172
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1561 0.0550 -2.8380 0.0045 -0.2638 -0.0483 **
## scale2 -0.0237 0.0099 -2.3835 0.0172 -0.0431 -0.0042 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.4114 -54.8228 -50.8228 -48.9339 -50.0728
##
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0514
## I^2 (total heterogeneity / total variability): 94.95%
## H^2 (total variability / sampling variability): 19.80
##
## Test for Heterogeneity:
## Q(df = 19) = 249.1823, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1327 0.0126 -10.5575 <.0001 -0.1573 -0.1080 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4154 -20.8308 -16.8308 -14.9419 -16.0808
##
## tau^2 (estimated amount of total heterogeneity): 0.0149 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.1220
## I^2 (total heterogeneity / total variability): 94.07%
## H^2 (total variability / sampling variability): 16.86
##
## Test for Heterogeneity:
## Q(df = 19) = 515.6235, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3230 0.0304 -10.6238 <.0001 -0.3826 -0.2634 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 33.0924 -66.1849 -62.1849 -60.2960 -61.4349
##
## tau^2 (estimated amount of total heterogeneity): 0.0008 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0286
## I^2 (total heterogeneity / total variability): 74.93%
## H^2 (total variability / sampling variability): 3.99
##
## Test for Heterogeneity:
## Q(df = 19) = 65.2235, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0305 0.0087 3.5238 0.0004 0.0135 0.0474 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 23.1317 -46.2634 -36.2634 -32.4005 -30.2634
##
## tau^2 (estimated amount of residual heterogeneity): 0.0026 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0510
## I^2 (residual heterogeneity / unaccounted variability): 92.98%
## H^2 (unaccounted variability / sampling variability): 14.24
## R^2 (amount of heterogeneity accounted for): 1.76%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 111.2689, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.0535, p-val = 0.3835
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1708 0.0678 -2.5187 0.0118 -0.3037 -0.0379 *
## continentEurope 0.0324 0.0691 0.4689 0.6392 -0.1031 0.1679
## continentNorth America 0.0733 0.0875 0.8375 0.4023 -0.0983 0.2449
## continentOceania 0.1131 0.0849 1.3323 0.1827 -0.0533 0.2796
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.1149 -18.2297 -8.2297 -4.3668 -2.2297
##
## tau^2 (estimated amount of residual heterogeneity): 0.0134 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.1158
## I^2 (residual heterogeneity / unaccounted variability): 91.48%
## H^2 (unaccounted variability / sampling variability): 11.73
## R^2 (amount of heterogeneity accounted for): 9.83%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 121.4474, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.7999, p-val = 0.2839
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3943 0.1770 -2.2270 0.0259 -0.7413 -0.0473 *
## continentEurope 0.0577 0.1798 0.3210 0.7482 -0.2947 0.4101
## continentNorth America 0.0879 0.2200 0.3994 0.6896 -0.3434 0.5191
## continentOceania 0.2869 0.2118 1.3543 0.1757 -0.1283 0.7021
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0215
## I^2 (residual heterogeneity / unaccounted variability): 55.39%
## H^2 (unaccounted variability / sampling variability): 2.24
## R^2 (amount of heterogeneity accounted for): 43.10%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 33.3571, p-val = 0.0066
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.7310, p-val = 0.0519
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0566 0.0647 0.8752 0.3814 -0.0702 0.1834
## continentEurope -0.0196 0.0652 -0.3008 0.7636 -0.1473 0.1081
## continentNorth America -0.1015 0.0736 -1.3792 0.1678 -0.2456 0.0427
## continentOceania -0.0598 0.0684 -0.8744 0.3819 -0.1938 0.0742
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.7124 -55.4249 -49.4249 -46.7538 -47.7106
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0448
## I^2 (residual heterogeneity / unaccounted variability): 93.08%
## H^2 (unaccounted variability / sampling variability): 14.46
## R^2 (amount of heterogeneity accounted for): 24.13%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 151.6387, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.3602, p-val = 0.0206
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1124 0.1060 1.0605 0.2889 -0.0954 0.3202
## mean.age -0.0040 0.0017 -2.3152 0.0206 -0.0073 -0.0006 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3143 -18.6286 -12.6286 -9.9575 -10.9143
##
## tau^2 (estimated amount of residual heterogeneity): 0.0161 (SE = 0.0065)
## tau (square root of estimated tau^2 value): 0.1268
## I^2 (residual heterogeneity / unaccounted variability): 94.24%
## H^2 (unaccounted variability / sampling variability): 17.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 512.6885, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0180, p-val = 0.8934
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2836 0.2995 -0.9469 0.3437 -0.8707 0.3035
## mean.age -0.0006 0.0048 -0.1340 0.8934 -0.0101 0.0088
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0009 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0298
## I^2 (residual heterogeneity / unaccounted variability): 75.22%
## H^2 (unaccounted variability / sampling variability): 4.04
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 56.3746, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4642, p-val = 0.4957
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0216 0.0772 -0.2796 0.7798 -0.1728 0.1297
## mean.age 0.0009 0.0013 0.6813 0.4957 -0.0016 0.0034
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.2693 -52.5387 -46.5387 -43.8675 -44.8244
##
## tau^2 (estimated amount of residual heterogeneity): 0.0026 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0506
## I^2 (residual heterogeneity / unaccounted variability): 93.12%
## H^2 (unaccounted variability / sampling variability): 14.54
## R^2 (amount of heterogeneity accounted for): 3.16%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 237.1471, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7479, p-val = 0.1861
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1694 0.0305 -5.5548 <.0001 -0.2291 -0.1096 ***
## scale2 0.0072 0.0054 1.3221 0.1861 -0.0035 0.0179
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.5142 -19.0284 -13.0284 -10.3572 -11.3141
##
## tau^2 (estimated amount of residual heterogeneity): 0.0152 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.1233
## I^2 (residual heterogeneity / unaccounted variability): 92.26%
## H^2 (unaccounted variability / sampling variability): 12.92
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 272.3729, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4215, p-val = 0.5162
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2784 0.0755 -3.6892 0.0002 -0.4263 -0.1305 ***
## scale2 -0.0087 0.0134 -0.6492 0.5162 -0.0350 0.0176
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.2850 -62.5700 -56.5700 -53.8989 -54.8557
##
## tau^2 (estimated amount of residual heterogeneity): 0.0009 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0301
## I^2 (residual heterogeneity / unaccounted variability): 71.50%
## H^2 (unaccounted variability / sampling variability): 3.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 63.8734, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2243, p-val = 0.2685
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0526 0.0217 2.4209 0.0155 0.0100 0.0952 *
## scale2 -0.0041 0.0037 -1.1065 0.2685 -0.0113 0.0031
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9259 -27.8517 -23.8517 -22.5736 -22.7608
##
## tau^2 (estimated amount of total heterogeneity): 0.0044 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0666
## I^2 (total heterogeneity / total variability): 96.63%
## H^2 (total variability / sampling variability): 29.66
##
## Test for Heterogeneity:
## Q(df = 14) = 88.0269, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1185 0.0214 -5.5462 <.0001 -0.1603 -0.0766 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 34.4955 -68.9910 -64.9910 -63.7129 -63.9001
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0166
## I^2 (total heterogeneity / total variability): 89.88%
## H^2 (total variability / sampling variability): 9.89
##
## Test for Heterogeneity:
## Q(df = 14) = 288.1623, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0027 0.0054 0.5039 0.6143 -0.0079 0.0134
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.5970 -25.1940 -19.1940 -17.4992 -16.5273
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0687
## I^2 (residual heterogeneity / unaccounted variability): 91.49%
## H^2 (unaccounted variability / sampling variability): 11.75
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 60.2360, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8314, p-val = 0.3619
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0595 0.0687 -0.8654 0.3868 -0.1942 0.0752
## continentEurope -0.0661 0.0725 -0.9118 0.3619 -0.2082 0.0760
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.8004 -65.6008 -59.6008 -57.9059 -56.9341
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0135
## I^2 (residual heterogeneity / unaccounted variability): 70.40%
## H^2 (unaccounted variability / sampling variability): 3.38
## R^2 (amount of heterogeneity accounted for): 34.06%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 31.5702, p-val = 0.0028
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.9410, p-val = 0.0471
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0225 0.0135 -1.6655 0.0958 -0.0489 0.0040 .
## continentEurope 0.0286 0.0144 1.9852 0.0471 0.0004 0.0568 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.3993 -26.7987 -20.7987 -19.1039 -18.1320
##
## tau^2 (estimated amount of residual heterogeneity): 0.0033 (SE = 0.0021)
## tau (square root of estimated tau^2 value): 0.0575
## I^2 (residual heterogeneity / unaccounted variability): 94.64%
## H^2 (unaccounted variability / sampling variability): 18.64
## R^2 (amount of heterogeneity accounted for): 25.65%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 58.6497, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8227, p-val = 0.0929
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1906 0.1836 1.0383 0.2991 -0.1692 0.5504
## mean.age -0.0051 0.0030 -1.6801 0.0929 -0.0109 0.0008 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.4643 -62.9287 -56.9287 -55.2338 -54.2620
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0175
## I^2 (residual heterogeneity / unaccounted variability): 89.46%
## H^2 (unaccounted variability / sampling variability): 9.49
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 223.7002, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0636, p-val = 0.8009
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0111 0.0552 -0.2011 0.8406 -0.1193 0.0971
## mean.age 0.0002 0.0009 0.2522 0.8009 -0.0015 0.0020
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.7211 -25.4422 -19.4422 -17.7474 -16.7756
##
## tau^2 (estimated amount of residual heterogeneity): 0.0046 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0677
## I^2 (residual heterogeneity / unaccounted variability): 91.72%
## H^2 (unaccounted variability / sampling variability): 12.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 71.7037, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0499, p-val = 0.3055
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2033 0.0854 -2.3816 0.0172 -0.3707 -0.0360 *
## scale2 0.0184 0.0179 1.0247 0.3055 -0.0168 0.0535
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.2277 -64.4555 -58.4555 -56.7606 -55.7888
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0152
## I^2 (residual heterogeneity / unaccounted variability): 77.86%
## H^2 (unaccounted variability / sampling variability): 4.52
## R^2 (amount of heterogeneity accounted for): 15.79%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 53.4215, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9509, p-val = 0.1625
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0242 0.0199 -1.2147 0.2245 -0.0632 0.0148
## scale2 0.0058 0.0041 1.3967 0.1625 -0.0023 0.0139
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.8090 -27.6179 -23.6179 -22.3398 -22.5270
##
## tau^2 (estimated amount of total heterogeneity): 0.0050 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0706
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.42
##
## Test for Heterogeneity:
## Q(df = 14) = 123.3776, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1336 0.0222 -6.0190 <.0001 -0.1772 -0.0901 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 34.8421 -69.6842 -65.6842 -64.4061 -64.5933
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0163
## I^2 (total heterogeneity / total variability): 89.81%
## H^2 (total variability / sampling variability): 9.81
##
## Test for Heterogeneity:
## Q(df = 14) = 308.8474, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0061 0.0053 1.1339 0.2568 -0.0044 0.0165
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.5214 -21.0428 -17.0428 -15.7647 -15.9519
##
## tau^2 (estimated amount of total heterogeneity): 0.0117 (SE = 0.0048)
## tau (square root of estimated tau^2 value): 0.1084
## I^2 (total heterogeneity / total variability): 95.36%
## H^2 (total variability / sampling variability): 21.54
##
## Test for Heterogeneity:
## Q(df = 14) = 429.0492, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2733 0.0292 -9.3732 <.0001 -0.3304 -0.2162 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.6867 -25.3733 -19.3733 -17.6785 -16.7066
##
## tau^2 (estimated amount of residual heterogeneity): 0.0050 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0708
## I^2 (residual heterogeneity / unaccounted variability): 92.28%
## H^2 (unaccounted variability / sampling variability): 12.96
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 72.8150, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1770, p-val = 0.2780
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0607 0.0709 -0.8569 0.3915 -0.1996 0.0782
## continentEurope -0.0810 0.0746 -1.0849 0.2780 -0.2272 0.0653
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 33.7294 -67.4587 -61.4587 -59.7639 -58.7920
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0116
## I^2 (residual heterogeneity / unaccounted variability): 64.45%
## H^2 (unaccounted variability / sampling variability): 2.81
## R^2 (amount of heterogeneity accounted for): 49.39%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 26.3451, p-val = 0.0153
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.5836, p-val = 0.0103
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0224 0.0116 -1.9217 0.0546 -0.0452 0.0004 .
## continentEurope 0.0321 0.0125 2.5658 0.0103 0.0076 0.0566 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.5751 -21.1502 -15.1502 -13.4553 -12.4835
##
## tau^2 (estimated amount of residual heterogeneity): 0.0101 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0.1005
## I^2 (residual heterogeneity / unaccounted variability): 93.23%
## H^2 (unaccounted variability / sampling variability): 14.78
## R^2 (amount of heterogeneity accounted for): 13.95%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 221.3757, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.9828, p-val = 0.0842
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1046 0.1011 -1.0343 0.3010 -0.3027 0.0936
## continentEurope -0.1813 0.1050 -1.7271 0.0842 -0.3870 0.0244 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.6138 -27.2275 -21.2275 -19.5327 -18.5608
##
## tau^2 (estimated amount of residual heterogeneity): 0.0035 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0592
## I^2 (residual heterogeneity / unaccounted variability): 95.12%
## H^2 (unaccounted variability / sampling variability): 20.48
## R^2 (amount of heterogeneity accounted for): 29.68%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 77.2955, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.5786, p-val = 0.0585
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2237 0.1882 1.1890 0.2344 -0.1451 0.5926
## mean.age -0.0058 0.0031 -1.8917 0.0585 -0.0118 0.0002 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.9349 -63.8698 -57.8698 -56.1750 -55.2032
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0169
## I^2 (residual heterogeneity / unaccounted variability): 89.12%
## H^2 (unaccounted variability / sampling variability): 9.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 227.8817, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3458, p-val = 0.5565
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0252 0.0536 -0.4707 0.6378 -0.1302 0.0798
## mean.age 0.0005 0.0009 0.5880 0.5565 -0.0012 0.0022
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.2935 -18.5870 -12.5870 -10.8921 -9.9203
##
## tau^2 (estimated amount of residual heterogeneity): 0.0127 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.1127
## I^2 (residual heterogeneity / unaccounted variability): 95.12%
## H^2 (unaccounted variability / sampling variability): 20.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 416.7204, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0706, p-val = 0.7904
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3632 0.3388 -1.0723 0.2836 -1.0272 0.3007
## mean.age 0.0014 0.0054 0.2658 0.7904 -0.0092 0.0120
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.7588 -25.5176 -19.5176 -17.8227 -16.8509
##
## tau^2 (estimated amount of residual heterogeneity): 0.0050 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0707
## I^2 (residual heterogeneity / unaccounted variability): 92.67%
## H^2 (unaccounted variability / sampling variability): 13.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 91.0687, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2774, p-val = 0.2584
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2304 0.0885 -2.6050 0.0092 -0.4038 -0.0571 **
## scale2 0.0211 0.0187 1.1302 0.2584 -0.0155 0.0577
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.2197 -64.4394 -58.4394 -56.7446 -55.7727
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0159
## I^2 (residual heterogeneity / unaccounted variability): 79.79%
## H^2 (unaccounted variability / sampling variability): 4.95
## R^2 (amount of heterogeneity accounted for): 4.62%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 64.3544, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0759, p-val = 0.2996
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0146 0.0206 -0.7109 0.4771 -0.0550 0.0257
## scale2 0.0045 0.0043 1.0372 0.2996 -0.0040 0.0129
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.3546 -20.7092 -14.7092 -13.0143 -12.0425
##
## tau^2 (estimated amount of residual heterogeneity): 0.0103 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0.1013
## I^2 (residual heterogeneity / unaccounted variability): 94.34%
## H^2 (unaccounted variability / sampling variability): 17.66
## R^2 (amount of heterogeneity accounted for): 12.71%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 224.6158, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.4730, p-val = 0.1158
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0903 0.1193 -0.7570 0.4490 -0.3240 0.1434
## scale2 -0.0413 0.0262 -1.5726 0.1158 -0.0927 0.0102
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5214 -5.0428 -1.0428 -3.6565 10.9572
##
## tau^2 (estimated amount of total heterogeneity): 0.0047 (SE = 0.0047)
## tau (square root of estimated tau^2 value): 0.0683
## I^2 (total heterogeneity / total variability): 98.86%
## H^2 (total variability / sampling variability): 87.84
##
## Test for Heterogeneity:
## Q(df = 2) = 259.7931, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4651 0.0398 11.6981 <.0001 0.3872 0.5430 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9216 -1.8432 4.1568 -1.8432 28.1568
##
## tau^2 (estimated amount of residual heterogeneity): 0.0092 (SE = 0.0131)
## tau (square root of estimated tau^2 value): 0.0961
## I^2 (residual heterogeneity / unaccounted variability): 99.61%
## H^2 (unaccounted variability / sampling variability): 254.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 254.1484, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0066, p-val = 0.9353
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4714 0.0969 4.8646 <.0001 0.2815 0.6614 ***
## continentEurope -0.0096 0.1184 -0.0812 0.9353 -0.2417 0.2225
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9271 -1.8542 4.1458 -1.8542 28.1458
##
## tau^2 (estimated amount of residual heterogeneity): 0.0091 (SE = 0.0130)
## tau (square root of estimated tau^2 value): 0.0955
## I^2 (residual heterogeneity / unaccounted variability): 99.50%
## H^2 (unaccounted variability / sampling variability): 201.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 201.2853, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0187, p-val = 0.8911
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5193 0.4001 1.2979 0.1943 -0.2649 1.3034
## mean.age -0.0010 0.0070 -0.1369 0.8911 -0.0147 0.0128
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7698 -3.5395 2.4605 -3.5395 26.4605
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0402
## I^2 (residual heterogeneity / unaccounted variability): 94.88%
## H^2 (unaccounted variability / sampling variability): 19.51
## R^2 (amount of heterogeneity accounted for): 65.44%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 19.5149, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6515, p-val = 0.0310
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2042 0.1234 1.6541 0.0981 -0.0377 0.4461 .
## scale1 0.0271 0.0125 2.1567 0.0310 0.0025 0.0517 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.5915 -9.1831 -5.1831 -7.7968 6.8169
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0234
## I^2 (total heterogeneity / total variability): 90.96%
## H^2 (total variability / sampling variability): 11.06
##
## Test for Heterogeneity:
## Q(df = 2) = 29.6831, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0988 0.0144 -6.8649 <.0001 -0.1271 -0.0706 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0979 -4.1957 1.8043 -4.1957 25.8043
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0291
## I^2 (residual heterogeneity / unaccounted variability): 95.88%
## H^2 (unaccounted variability / sampling variability): 24.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 24.2752, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2569, p-val = 0.6122
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0849 0.0319 -2.6640 0.0077 -0.1474 -0.0224 **
## continentEurope -0.0193 0.0382 -0.5069 0.6122 -0.0941 0.0555
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2421 -4.4842 1.5158 -4.4842 25.5158
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0248
## I^2 (residual heterogeneity / unaccounted variability): 93.09%
## H^2 (unaccounted variability / sampling variability): 14.47
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.4671, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6968, p-val = 0.4039
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1906 0.1112 -1.7148 0.0864 -0.4085 0.0272 .
## mean.age 0.0016 0.0020 0.8347 0.4039 -0.0022 0.0055
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1509 -4.3019 1.6981 -4.3019 25.6981
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0265
## I^2 (residual heterogeneity / unaccounted variability): 88.35%
## H^2 (unaccounted variability / sampling variability): 8.58
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.5805, p-val = 0.0034
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5018, p-val = 0.4787
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0407 0.0833 -0.4882 0.6254 -0.2039 0.1226
## scale1 -0.0060 0.0085 -0.7084 0.4787 -0.0226 0.0106
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4499 -8.8997 -4.8997 -7.5134 7.1003
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0253
## I^2 (total heterogeneity / total variability): 92.12%
## H^2 (total variability / sampling variability): 12.69
##
## Test for Heterogeneity:
## Q(df = 2) = 35.0306, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0992 0.0154 -6.4215 <.0001 -0.1294 -0.0689 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9940 -3.9880 2.0120 -3.9880 26.0120
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0324
## I^2 (residual heterogeneity / unaccounted variability): 96.59%
## H^2 (unaccounted variability / sampling variability): 29.34
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 29.3438, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1877, p-val = 0.6648
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0862 0.0349 -2.4722 0.0134 -0.1545 -0.0179 *
## continentEurope -0.0182 0.0419 -0.4332 0.6648 -0.1003 0.0640
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1187 -4.2374 1.7626 -4.2374 25.7626
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0283
## I^2 (residual heterogeneity / unaccounted variability): 94.52%
## H^2 (unaccounted variability / sampling variability): 18.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 18.2329, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5399, p-val = 0.4625
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1896 0.1246 -1.5217 0.1281 -0.4339 0.0546
## mean.age 0.0016 0.0022 0.7348 0.4625 -0.0027 0.0059
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1224 -4.2449 1.7551 -4.2449 25.7551
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0274
## I^2 (residual heterogeneity / unaccounted variability): 89.23%
## H^2 (unaccounted variability / sampling variability): 9.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.2826, p-val = 0.0023
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6381, p-val = 0.4244
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0316 0.0860 -0.3679 0.7130 -0.2001 0.1369
## scale1 -0.0070 0.0087 -0.7988 0.4244 -0.0241 0.0102
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4270 -8.8539 -4.8539 -7.4676 7.1461
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0257
## I^2 (total heterogeneity / total variability): 92.66%
## H^2 (total variability / sampling variability): 13.63
##
## Test for Heterogeneity:
## Q(df = 2) = 37.7822, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1062 0.0156 -6.8067 <.0001 -0.1367 -0.0756 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7277 -5.4553 -1.4553 -4.0690 10.5447
##
## tau^2 (estimated amount of total heterogeneity): 0.0034 (SE = 0.0038)
## tau (square root of estimated tau^2 value): 0.0579
## I^2 (total heterogeneity / total variability): 89.62%
## H^2 (total variability / sampling variability): 9.63
##
## Test for Heterogeneity:
## Q(df = 2) = 17.7405, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3902 0.0355 -10.9992 <.0001 -0.4598 -0.3207 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9794 -3.9588 2.0412 -3.9588 26.0412
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0329
## I^2 (residual heterogeneity / unaccounted variability): 96.83%
## H^2 (unaccounted variability / sampling variability): 31.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 31.5116, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1863, p-val = 0.6660
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0931 0.0352 -2.6476 0.0081 -0.1621 -0.0242 **
## continentEurope -0.0183 0.0424 -0.4316 0.6660 -0.1014 0.0648
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1054 -2.2107 3.7893 -2.2107 27.7893
##
## tau^2 (estimated amount of residual heterogeneity): 0.0061 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.0778
## I^2 (residual heterogeneity / unaccounted variability): 94.28%
## H^2 (unaccounted variability / sampling variability): 17.49
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.4937, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1869, p-val = 0.6655
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4184 0.0813 -5.1465 <.0001 -0.5777 -0.2591 ***
## continentEurope 0.0428 0.0991 0.4323 0.6655 -0.1513 0.2370
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1035 -4.2070 1.7930 -4.2070 25.7930
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0288
## I^2 (residual heterogeneity / unaccounted variability): 94.91%
## H^2 (unaccounted variability / sampling variability): 19.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 19.6275, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5351, p-val = 0.4645
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1974 0.1262 -1.5644 0.1177 -0.4447 0.0499
## mean.age 0.0016 0.0022 0.7315 0.4645 -0.0027 0.0060
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0337 -2.0674 3.9326 -2.0674 27.9326
##
## tau^2 (estimated amount of residual heterogeneity): 0.0070 (SE = 0.0105)
## tau (square root of estimated tau^2 value): 0.0836
## I^2 (residual heterogeneity / unaccounted variability): 94.30%
## H^2 (unaccounted variability / sampling variability): 17.54
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.5398, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0383, p-val = 0.8449
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3200 0.3582 -0.8934 0.3716 -1.0221 0.3821
## mean.age -0.0012 0.0063 -0.1956 0.8449 -0.0136 0.0111
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1104 -4.2209 1.7791 -4.2209 25.7791
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0278
## I^2 (residual heterogeneity / unaccounted variability): 90.15%
## H^2 (unaccounted variability / sampling variability): 10.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.1472, p-val = 0.0014
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6339, p-val = 0.4259
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0379 0.0872 -0.4347 0.6638 -0.2088 0.1330
## scale1 -0.0071 0.0089 -0.7961 0.4259 -0.0245 0.0103
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9457 -5.8913 0.1087 -5.8913 24.1087
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.2005, p-val = 0.6543
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 17.5400, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1236 0.0686 -1.8008 0.0717 -0.2581 0.0109 .
## scale1 -0.0276 0.0066 -4.1881 <.0001 -0.0406 -0.0147 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.9199 -9.8397 -5.8397 -8.4534 6.1603
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0169
## I^2 (total heterogeneity / total variability): 72.83%
## H^2 (total variability / sampling variability): 3.68
##
## Test for Heterogeneity:
## Q(df = 2) = 7.6797, p-val = 0.0215
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1236 0.0118 -10.5058 <.0001 -0.1466 -0.1005 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8282 -5.6564 -1.6564 -4.2701 10.3436
##
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0509
## I^2 (total heterogeneity / total variability): 80.09%
## H^2 (total variability / sampling variability): 5.02
##
## Test for Heterogeneity:
## Q(df = 2) = 11.3569, p-val = 0.0034
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3999 0.0344 -11.6395 <.0001 -0.4673 -0.3326 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2550 -8.5101 -4.5101 -7.1238 7.4899
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0245
## I^2 (total heterogeneity / total variability): 73.92%
## H^2 (total variability / sampling variability): 3.84
##
## Test for Heterogeneity:
## Q(df = 2) = 8.3021, p-val = 0.0157
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0284 0.0168 1.6904 0.0910 -0.0045 0.0614 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6107 -5.2214 0.7786 -5.2214 24.7786
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0158
## I^2 (residual heterogeneity / unaccounted variability): 78.69%
## H^2 (unaccounted variability / sampling variability): 4.69
## R^2 (amount of heterogeneity accounted for): 12.53%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.6934, p-val = 0.0303
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0876, p-val = 0.2970
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1002 0.0254 -3.9433 <.0001 -0.1500 -0.0504 ***
## continentEurope -0.0295 0.0283 -1.0429 0.2970 -0.0850 0.0260
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3115 -2.6231 3.3769 -2.6231 27.3769
##
## tau^2 (estimated amount of residual heterogeneity): 0.0039 (SE = 0.0060)
## tau (square root of estimated tau^2 value): 0.0622
## I^2 (residual heterogeneity / unaccounted variability): 90.99%
## H^2 (unaccounted variability / sampling variability): 11.10
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.0986, p-val = 0.0009
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3116, p-val = 0.5767
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4420 0.0844 -5.2359 <.0001 -0.6074 -0.2765 ***
## continentEurope 0.0537 0.0961 0.5582 0.5767 -0.1348 0.2421
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0319
## I^2 (residual heterogeneity / unaccounted variability): 87.75%
## H^2 (unaccounted variability / sampling variability): 8.16
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.1646, p-val = 0.0043
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2306, p-val = 0.6311
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0116 0.0409 0.2838 0.7766 -0.0685 0.0917
## continentEurope 0.0227 0.0474 0.4802 0.6311 -0.0701 0.1156
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9651 -5.9303 0.0697 -5.9303 24.0697
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0083
## I^2 (residual heterogeneity / unaccounted variability): 44.03%
## H^2 (unaccounted variability / sampling variability): 1.79
## R^2 (amount of heterogeneity accounted for): 75.91%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.7868, p-val = 0.1813
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.2222, p-val = 0.0726
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2486 0.0675 -3.6809 0.0002 -0.3809 -0.1162 ***
## mean.age 0.0023 0.0013 1.7951 0.0726 -0.0002 0.0048 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1917 -2.3833 3.6167 -2.3833 27.6167
##
## tau^2 (estimated amount of residual heterogeneity): 0.0049 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0698
## I^2 (residual heterogeneity / unaccounted variability): 90.29%
## H^2 (unaccounted variability / sampling variability): 10.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.3005, p-val = 0.0013
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0803, p-val = 0.7768
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3059 0.3386 -0.9034 0.3663 -0.9696 0.3578
## mean.age -0.0017 0.0061 -0.2834 0.7768 -0.0137 0.0102
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0020)
## tau (square root of estimated tau^2 value): 0.0351
## I^2 (residual heterogeneity / unaccounted variability): 87.33%
## H^2 (unaccounted variability / sampling variability): 7.89
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.8908, p-val = 0.0050
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0504, p-val = 0.8223
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0655 0.1664 0.3937 0.6938 -0.2607 0.3917
## mean.age -0.0007 0.0030 -0.2246 0.8223 -0.0065 0.0052
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1338 -4.2677 1.7323 -4.2677 25.7323
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0247
## I^2 (residual heterogeneity / unaccounted variability): 74.14%
## H^2 (unaccounted variability / sampling variability): 3.87
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.8673, p-val = 0.0492
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0808, p-val = 0.7763
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0990 0.0813 -1.2185 0.2230 -0.2583 0.0603
## scale1 -0.0024 0.0083 -0.2842 0.7763 -0.0187 0.0139
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2520 -4.5040 1.4960 -4.5040 25.4960
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0290, p-val = 0.8647
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.3279, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1777 0.0710 -2.5038 0.0123 -0.3167 -0.0386 *
## scale1 -0.0232 0.0069 -3.3657 0.0008 -0.0367 -0.0097 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0540 -6.1080 -0.1080 -6.1080 23.8920
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0004, p-val = 0.9850
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.3018, p-val = 0.0040
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1445 0.0434 3.3278 0.0009 0.0594 0.2296 ***
## scale1 -0.0120 0.0042 -2.8813 0.0040 -0.0202 -0.0038 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2455 -6.4910 -2.4910 -5.1047 9.5090
##
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0474
## I^2 (total heterogeneity / total variability): 98.09%
## H^2 (total variability / sampling variability): 52.46
##
## Test for Heterogeneity:
## Q(df = 2) = 154.7428, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4690 0.0277 16.9062 <.0001 0.4146 0.5234 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2878 -2.5757 3.4243 -2.5757 27.4243
##
## tau^2 (estimated amount of residual heterogeneity): 0.0044 (SE = 0.0063)
## tau (square root of estimated tau^2 value): 0.0665
## I^2 (residual heterogeneity / unaccounted variability): 99.32%
## H^2 (unaccounted variability / sampling variability): 146.66
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 146.6625, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0107, p-val = 0.9175
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4632 0.0674 6.8731 <.0001 0.3311 0.5952 ***
## continentEurope 0.0085 0.0823 0.1036 0.9175 -0.1527 0.1698
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3341 -2.6682 3.3318 -2.6682 27.3318
##
## tau^2 (estimated amount of residual heterogeneity): 0.0040 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0.0634
## I^2 (residual heterogeneity / unaccounted variability): 99.10%
## H^2 (unaccounted variability / sampling variability): 110.74
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 110.7419, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1105, p-val = 0.7396
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5567 0.2667 2.0873 0.0369 0.0340 1.0794 *
## mean.age -0.0016 0.0047 -0.3324 0.7396 -0.0107 0.0076
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8179 -3.6358 2.3642 -3.6358 26.3642
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0384
## I^2 (residual heterogeneity / unaccounted variability): 95.77%
## H^2 (unaccounted variability / sampling variability): 23.61
## R^2 (amount of heterogeneity accounted for): 34.33%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 23.6150, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9871, p-val = 0.1586
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3061 0.1178 2.5982 0.0094 0.0752 0.5371 **
## scale1 0.0169 0.0120 1.4096 0.1586 -0.0066 0.0404
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5618 -7.1236 -3.1236 -5.7373 8.8764
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0401
## I^2 (total heterogeneity / total variability): 96.56%
## H^2 (total variability / sampling variability): 29.11
##
## Test for Heterogeneity:
## Q(df = 2) = 80.5987, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1665 0.0238 -7.0012 <.0001 -0.2132 -0.1199 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5930 -3.1861 2.8139 -3.1861 26.8139
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0488
## I^2 (residual heterogeneity / unaccounted variability): 98.47%
## H^2 (unaccounted variability / sampling variability): 65.50
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 65.5047, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3328, p-val = 0.5640
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1420 0.0509 -2.7915 0.0052 -0.2417 -0.0423 **
## continentEurope -0.0356 0.0616 -0.5769 0.5640 -0.1563 0.0852
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7540 -3.5079 2.4921 -3.5079 26.4921
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0413
## I^2 (residual heterogeneity / unaccounted variability): 97.28%
## H^2 (unaccounted variability / sampling variability): 36.80
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 36.7987, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8458, p-val = 0.3577
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3285 0.1778 -1.8472 0.0647 -0.6770 0.0200 .
## mean.age 0.0029 0.0031 0.9197 0.3577 -0.0033 0.0090
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5868 -3.1737 2.8263 -3.1737 26.8263
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0035)
## tau (square root of estimated tau^2 value): 0.0484
## I^2 (residual heterogeneity / unaccounted variability): 95.56%
## H^2 (unaccounted variability / sampling variability): 22.50
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 22.4984, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3561, p-val = 0.5507
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0795 0.1480 -0.5374 0.5910 -0.3696 0.2106
## scale1 -0.0090 0.0151 -0.5968 0.5507 -0.0385 0.0205
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.4836 -6.9672 -2.9672 -5.5809 9.0328
##
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0418
## I^2 (total heterogeneity / total variability): 96.86%
## H^2 (total variability / sampling variability): 31.87
##
## Test for Heterogeneity:
## Q(df = 2) = 92.6201, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1641 0.0248 -6.6267 <.0001 -0.2126 -0.1155 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5049 -3.0098 2.9902 -3.0098 26.9902
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0534
## I^2 (residual heterogeneity / unaccounted variability): 98.73%
## H^2 (unaccounted variability / sampling variability): 78.87
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.8729, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2103, p-val = 0.6465
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1429 0.0552 -2.5857 0.0097 -0.2511 -0.0346 **
## continentEurope -0.0307 0.0670 -0.4586 0.6465 -0.1622 0.1007
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6356 -3.2713 2.7287 -3.2713 26.7287
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0466
## I^2 (residual heterogeneity / unaccounted variability): 97.88%
## H^2 (unaccounted variability / sampling variability): 47.07
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 47.0668, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5780, p-val = 0.4471
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3140 0.1994 -1.5751 0.1152 -0.7048 0.0767
## mean.age 0.0027 0.0035 0.7603 0.4471 -0.0042 0.0096
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6095 -3.2190 2.7810 -3.2190 26.7810
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0033)
## tau (square root of estimated tau^2 value): 0.0473
## I^2 (residual heterogeneity / unaccounted variability): 95.38%
## H^2 (unaccounted variability / sampling variability): 21.66
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 21.6626, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5376, p-val = 0.4634
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0598 0.1446 -0.4135 0.6792 -0.3433 0.2237
## scale1 -0.0108 0.0147 -0.7332 0.4634 -0.0396 0.0180
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6340 -7.2679 -3.2679 -5.8816 8.7321
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0388
## I^2 (total heterogeneity / total variability): 96.49%
## H^2 (total variability / sampling variability): 28.51
##
## Test for Heterogeneity:
## Q(df = 2) = 84.1707, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1694 0.0230 -7.3613 <.0001 -0.2145 -0.1243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7446 -5.4893 -1.4893 -4.1030 10.5107
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0559
## I^2 (total heterogeneity / total variability): 88.11%
## H^2 (total variability / sampling variability): 8.41
##
## Test for Heterogeneity:
## Q(df = 2) = 12.5036, p-val = 0.0019
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3711 0.0346 -10.7347 <.0001 -0.4389 -0.3034 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5577 -3.1154 2.8846 -3.1154 26.8846
##
## tau^2 (estimated amount of residual heterogeneity): 0.0026 (SE = 0.0037)
## tau (square root of estimated tau^2 value): 0.0506
## I^2 (residual heterogeneity / unaccounted variability): 98.63%
## H^2 (unaccounted variability / sampling variability): 73.03
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 73.0256, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1537, p-val = 0.6950
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1521 0.0525 -2.8990 0.0037 -0.2549 -0.0493 **
## continentEurope -0.0250 0.0636 -0.3920 0.6950 -0.1497 0.0998
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0787 -4.1573 1.8427 -4.1573 25.8427
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0232
## I^2 (residual heterogeneity / unaccounted variability): 58.95%
## H^2 (unaccounted variability / sampling variability): 2.44
## R^2 (amount of heterogeneity accounted for): 82.72%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.4358, p-val = 0.1186
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.7102, p-val = 0.0169
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4401 0.0348 -12.6362 <.0001 -0.5084 -0.3719 ***
## continentEurope 0.0969 0.0406 2.3896 0.0169 0.0174 0.1764 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6719 -3.3439 2.6561 -3.3439 26.6561
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0450
## I^2 (residual heterogeneity / unaccounted variability): 97.79%
## H^2 (unaccounted variability / sampling variability): 45.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 45.1925, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4563, p-val = 0.4993
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2978 0.1923 -1.5490 0.1214 -0.6746 0.0790
## mean.age 0.0023 0.0034 0.6755 0.4993 -0.0044 0.0089
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6296 -3.2591 2.7409 -3.2591 26.7409
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0426
## I^2 (residual heterogeneity / unaccounted variability): 80.54%
## H^2 (unaccounted variability / sampling variability): 5.14
## R^2 (amount of heterogeneity accounted for): 42.03%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 5.1400, p-val = 0.0234
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.2145, p-val = 0.1367
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0784 0.1983 -0.3954 0.6926 -0.4670 0.3102
## mean.age -0.0052 0.0035 -1.4881 0.1367 -0.0121 0.0017
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7298 -3.4597 2.5403 -3.4597 26.5403
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0417
## I^2 (residual heterogeneity / unaccounted variability): 94.47%
## H^2 (unaccounted variability / sampling variability): 18.09
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 18.0930, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6871, p-val = 0.4071
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0651 0.1281 -0.5082 0.6113 -0.3161 0.1859
## scale1 -0.0108 0.0130 -0.8289 0.4071 -0.0363 0.0147
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4460 -2.8920 3.1080 -2.8920 27.1080
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 87.18%
## H^2 (unaccounted variability / sampling variability): 7.80
## R^2 (amount of heterogeneity accounted for): 9.41%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.8003, p-val = 0.0052
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2679, p-val = 0.2602
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1769 0.1756 -1.0072 0.3139 -0.5210 0.1673
## scale1 -0.0200 0.0177 -1.1260 0.2602 -0.0548 0.0148
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2201 -6.4403 -2.4403 -5.0540 9.5597
##
## tau^2 (estimated amount of total heterogeneity): 0.0022 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0465
## I^2 (total heterogeneity / total variability): 95.06%
## H^2 (total variability / sampling variability): 20.23
##
## Test for Heterogeneity:
## Q(df = 2) = 44.3578, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1815 0.0280 -6.4846 <.0001 -0.2364 -0.1266 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.3739 -8.7478 -4.7478 -7.3615 7.2522
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 1.3656, p-val = 0.5052
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3612 0.0111 -32.4420 <.0001 -0.3830 -0.3394 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7722 -7.5444 -3.5444 -6.1581 8.4556
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0267
## I^2 (total heterogeneity / total variability): 76.36%
## H^2 (total variability / sampling variability): 4.23
##
## Test for Heterogeneity:
## Q(df = 2) = 5.6916, p-val = 0.0581
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0295 0.0182 1.6258 0.1040 -0.0061 0.0652
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6469 -3.2938 2.7062 -3.2938 26.7062
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0459
## I^2 (residual heterogeneity / unaccounted variability): 96.82%
## H^2 (unaccounted variability / sampling variability): 31.41
## R^2 (amount of heterogeneity accounted for): 2.61%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 31.4106, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0296, p-val = 0.3103
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1383 0.0508 -2.7214 0.0065 -0.2379 -0.0387 **
## continentEurope -0.0615 0.0606 -1.0147 0.3103 -0.1802 0.0573
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4740 -4.9480 1.0520 -4.9480 25.0520
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0047
## I^2 (residual heterogeneity / unaccounted variability): 5.24%
## H^2 (unaccounted variability / sampling variability): 1.06
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0553, p-val = 0.3043
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3153, p-val = 0.5744
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3949 0.0617 -6.4017 <.0001 -0.5158 -0.2740 ***
## continentEurope 0.0353 0.0628 0.5615 0.5744 -0.0879 0.1584
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9965, p-val = 0.3182
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6951, p-val = 0.0302
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0229 0.0282 -0.8114 0.4171 -0.0781 0.0324
## continentEurope 0.0627 0.0289 2.1668 0.0302 0.0060 0.1194 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9370 -3.8741 2.1259 -3.8741 26.1259
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0335
## I^2 (residual heterogeneity / unaccounted variability): 92.46%
## H^2 (unaccounted variability / sampling variability): 13.25
## R^2 (amount of heterogeneity accounted for): 47.94%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.2544, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.5665, p-val = 0.1092
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4296 0.1550 -2.7710 0.0056 -0.7334 -0.1257 **
## mean.age 0.0044 0.0028 1.6020 0.1092 -0.0010 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1745 -4.3490 1.6510 -4.3490 25.6510
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0142
## I^2 (residual heterogeneity / unaccounted variability): 26.76%
## H^2 (unaccounted variability / sampling variability): 1.37
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3653, p-val = 0.2426
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0329, p-val = 0.8562
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3274 0.1774 -1.8456 0.0650 -0.6750 0.0203 .
## mean.age -0.0006 0.0034 -0.1813 0.8562 -0.0074 0.0061
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0192
## I^2 (residual heterogeneity / unaccounted variability): 66.82%
## H^2 (unaccounted variability / sampling variability): 3.01
## R^2 (amount of heterogeneity accounted for): 48.10%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.0137, p-val = 0.0826
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.4094, p-val = 0.1206
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2115 0.1165 1.8149 0.0695 -0.0169 0.4398 .
## mean.age -0.0033 0.0022 -1.5522 0.1206 -0.0076 0.0009
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2943 -2.5886 3.4114 -2.5886 27.4114
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.0644
## I^2 (residual heterogeneity / unaccounted variability): 94.24%
## H^2 (unaccounted variability / sampling variability): 17.36
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.3583, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0712, p-val = 0.7896
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1287 0.1972 -0.6524 0.5142 -0.5152 0.2579
## scale1 -0.0054 0.0201 -0.2669 0.7896 -0.0447 0.0340
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0892 -4.1785 1.8215 -4.1785 25.8215
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.2123, p-val = 0.6450
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1533, p-val = 0.2829
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2846 0.0722 -3.9426 <.0001 -0.4261 -0.1431 ***
## scale1 -0.0075 0.0070 -1.0739 0.2829 -0.0212 0.0062
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7479 -3.4959 2.5041 -3.4959 26.5041
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0368
## I^2 (residual heterogeneity / unaccounted variability): 76.13%
## H^2 (unaccounted variability / sampling variability): 4.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.1900, p-val = 0.0407
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6711, p-val = 0.4127
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1242 0.1207 1.0292 0.3034 -0.1124 0.3608
## scale1 -0.0101 0.0123 -0.8192 0.4127 -0.0343 0.0141
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7922 -5.5843 -1.5843 -4.1980 10.4157
##
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0593
## I^2 (total heterogeneity / total variability): 98.14%
## H^2 (total variability / sampling variability): 53.64
##
## Test for Heterogeneity:
## Q(df = 2) = 120.8490, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4100 0.0346 11.8384 <.0001 0.3421 0.4779 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1281 -2.2562 3.7438 -2.2562 27.7438
##
## tau^2 (estimated amount of residual heterogeneity): 0.0061 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.0780
## I^2 (residual heterogeneity / unaccounted variability): 99.17%
## H^2 (unaccounted variability / sampling variability): 120.41
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 120.4055, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1629, p-val = 0.6865
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4361 0.0789 5.5266 <.0001 0.2814 0.5907 ***
## continentEurope -0.0389 0.0964 -0.4036 0.6865 -0.2278 0.1500
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0658 -2.1315 3.8685 -2.1315 27.8685
##
## tau^2 (estimated amount of residual heterogeneity): 0.0069 (SE = 0.0098)
## tau (square root of estimated tau^2 value): 0.0830
## I^2 (residual heterogeneity / unaccounted variability): 99.13%
## H^2 (unaccounted variability / sampling variability): 114.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 114.6449, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0284, p-val = 0.8661
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3518 0.3481 1.0107 0.3122 -0.3305 1.0341
## mean.age 0.0010 0.0061 0.1687 0.8661 -0.0109 0.0130
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0494 -6.0989 -0.0989 -6.0989 23.9011
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0070
## I^2 (residual heterogeneity / unaccounted variability): 36.98%
## H^2 (unaccounted variability / sampling variability): 1.59
## R^2 (amount of heterogeneity accounted for): 98.62%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.5869, p-val = 0.2078
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 62.9189, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1554 0.0336 4.6313 <.0001 0.0896 0.2212 ***
## scale1 0.0266 0.0034 7.9321 <.0001 0.0200 0.0332 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.9741 -9.9482 -5.9482 -8.5620 6.0518
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 85.30%
## H^2 (total variability / sampling variability): 6.81
##
## Test for Heterogeneity:
## Q(df = 2) = 18.1974, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1745 0.0122 -14.2895 <.0001 -0.1984 -0.1506 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2371 -4.4743 1.5257 -4.4743 25.5257
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0251
## I^2 (residual heterogeneity / unaccounted variability): 94.11%
## H^2 (unaccounted variability / sampling variability): 16.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.9772, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0458, p-val = 0.8304
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1686 0.0293 -5.7480 <.0001 -0.2261 -0.1111 ***
## continentEurope -0.0074 0.0346 -0.2141 0.8304 -0.0751 0.0603
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3089 -4.6178 1.3822 -4.6178 25.3822
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0230
## I^2 (residual heterogeneity / unaccounted variability): 91.23%
## H^2 (unaccounted variability / sampling variability): 11.40
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.3967, p-val = 0.0007
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2316, p-val = 0.6303
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2250 0.1067 -2.1089 0.0350 -0.4341 -0.0159 *
## mean.age 0.0009 0.0019 0.4813 0.6303 -0.0028 0.0047
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6147 -5.2294 0.7706 -5.2294 24.7706
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0137
## I^2 (residual heterogeneity / unaccounted variability): 60.25%
## H^2 (unaccounted variability / sampling variability): 2.52
## R^2 (amount of heterogeneity accounted for): 47.56%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5156, p-val = 0.1127
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0618, p-val = 0.1510
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1076 0.0482 -2.2330 0.0255 -0.2020 -0.0132 *
## scale1 -0.0071 0.0049 -1.4359 0.1510 -0.0167 0.0026
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0903 -10.1806 -6.1806 -8.7943 5.8194
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0177
## I^2 (total heterogeneity / total variability): 83.51%
## H^2 (total variability / sampling variability): 6.06
##
## Test for Heterogeneity:
## Q(df = 2) = 15.9953, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1745 0.0115 -15.1458 <.0001 -0.1970 -0.1519 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3014 -4.6029 1.3971 -4.6029 25.3971
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0234
## I^2 (residual heterogeneity / unaccounted variability): 93.31%
## H^2 (unaccounted variability / sampling variability): 14.94
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.9394, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0434, p-val = 0.8349
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1690 0.0279 -6.0617 <.0001 -0.2236 -0.1143 ***
## continentEurope -0.0068 0.0327 -0.2084 0.8349 -0.0709 0.0573
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3716 -4.7433 1.2567 -4.7433 25.2567
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0214
## I^2 (residual heterogeneity / unaccounted variability): 90.08%
## H^2 (unaccounted variability / sampling variability): 10.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.0815, p-val = 0.0015
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2281, p-val = 0.6329
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2218 0.1008 -2.1996 0.0278 -0.4194 -0.0242 *
## mean.age 0.0009 0.0018 0.4776 0.6329 -0.0027 0.0044
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6875 -5.3749 0.6251 -5.3749 24.6251
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0122
## I^2 (residual heterogeneity / unaccounted variability): 54.59%
## H^2 (unaccounted variability / sampling variability): 2.20
## R^2 (amount of heterogeneity accounted for): 52.77%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2019, p-val = 0.1378
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.2773, p-val = 0.1313
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1104 0.0440 -2.5083 0.0121 -0.1967 -0.0241 *
## scale1 -0.0068 0.0045 -1.5091 0.1313 -0.0156 0.0020
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.2378 -10.4755 -6.4755 -9.0892 5.5245
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0163
## I^2 (total heterogeneity / total variability): 81.36%
## H^2 (total variability / sampling variability): 5.36
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8139, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1790 0.0107 -16.6951 <.0001 -0.2000 -0.1580 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7051 -5.4103 -1.4103 -4.0240 10.5897
##
## tau^2 (estimated amount of total heterogeneity): 0.0033 (SE = 0.0038)
## tau (square root of estimated tau^2 value): 0.0579
## I^2 (total heterogeneity / total variability): 88.49%
## H^2 (total variability / sampling variability): 8.69
##
## Test for Heterogeneity:
## Q(df = 2) = 15.4723, p-val = 0.0004
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2701 0.0357 -7.5584 <.0001 -0.3401 -0.2000 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3629 -4.7257 1.2743 -4.7257 25.2743
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0219
## I^2 (residual heterogeneity / unaccounted variability): 92.58%
## H^2 (unaccounted variability / sampling variability): 13.47
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.4695, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0001, p-val = 0.9911
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1784 0.0265 -6.7277 <.0001 -0.2303 -0.1264 ***
## continentEurope -0.0003 0.0310 -0.0111 0.9911 -0.0611 0.0604
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2371 -2.4741 3.5259 -2.4741 27.5259
##
## tau^2 (estimated amount of residual heterogeneity): 0.0046 (SE = 0.0070)
## tau (square root of estimated tau^2 value): 0.0676
## I^2 (residual heterogeneity / unaccounted variability): 92.64%
## H^2 (unaccounted variability / sampling variability): 13.58
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.5778, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5532, p-val = 0.4570
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3149 0.0730 -4.3124 <.0001 -0.4580 -0.1718 ***
## continentEurope 0.0657 0.0883 0.7438 0.4570 -0.1074 0.2387
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3837 -4.7673 1.2327 -4.7673 25.2327
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0212
## I^2 (residual heterogeneity / unaccounted variability): 90.04%
## H^2 (unaccounted variability / sampling variability): 10.04
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.0417, p-val = 0.0015
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0663, p-val = 0.7968
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2040 0.0995 -2.0499 0.0404 -0.3991 -0.0090 *
## mean.age 0.0005 0.0018 0.2575 0.7968 -0.0030 0.0040
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1014 -2.2027 3.7973 -2.2027 27.7973
##
## tau^2 (estimated amount of residual heterogeneity): 0.0060 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.0778
## I^2 (residual heterogeneity / unaccounted variability): 93.47%
## H^2 (unaccounted variability / sampling variability): 15.32
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.3185, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2059, p-val = 0.6500
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1183 0.3374 -0.3507 0.7258 -0.7796 0.5430
## mean.age -0.0027 0.0059 -0.4537 0.6500 -0.0143 0.0090
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0779 -6.1559 -0.1559 -6.1559 23.8441
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0022
## I^2 (residual heterogeneity / unaccounted variability): 3.98%
## H^2 (unaccounted variability / sampling variability): 1.04
## R^2 (amount of heterogeneity accounted for): 98.13%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0414, p-val = 0.3075
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.1985, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1079 0.0236 -4.5701 <.0001 -0.1541 -0.0616 ***
## scale1 -0.0077 0.0023 -3.3464 0.0008 -0.0122 -0.0032 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7433 -5.4866 0.5134 -5.4866 24.5134
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.3454, p-val = 0.5567
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 15.1269, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0181 0.0681 -0.2661 0.7902 -0.1516 0.1153
## scale1 -0.0256 0.0066 -3.8893 0.0001 -0.0386 -0.0127 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.5766 -11.1532 -7.1532 -9.7669 4.8468
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0007
## I^2 (total heterogeneity / total variability): 0.36%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 2.5699, p-val = 0.2767
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1835 0.0050 -36.6184 <.0001 -0.1933 -0.1737 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7396 -5.4793 -1.4793 -4.0930 10.5207
##
## tau^2 (estimated amount of total heterogeneity): 0.0032 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0563
## I^2 (total heterogeneity / total variability): 83.15%
## H^2 (total variability / sampling variability): 5.94
##
## Test for Heterogeneity:
## Q(df = 2) = 15.3064, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2463 0.0377 -6.5386 <.0001 -0.3201 -0.1725 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5762 -7.1524 -3.1524 -5.7661 8.8476
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0340
## I^2 (total heterogeneity / total variability): 82.52%
## H^2 (total variability / sampling variability): 5.72
##
## Test for Heterogeneity:
## Q(df = 2) = 9.9731, p-val = 0.0068
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0038 0.0223 -0.1686 0.8661 -0.0474 0.0399
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5988 -7.1976 -1.1976 -7.1976 22.8024
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4885, p-val = 0.4846
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0814, p-val = 0.1491
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1507 0.0233 -6.4584 <.0001 -0.1964 -0.1049 ***
## continentEurope -0.0345 0.0239 -1.4427 0.1491 -0.0813 0.0124
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1944 -2.3888 3.6112 -2.3888 27.6112
##
## tau^2 (estimated amount of residual heterogeneity): 0.0050 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0708
## I^2 (residual heterogeneity / unaccounted variability): 93.20%
## H^2 (unaccounted variability / sampling variability): 14.71
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.7062, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0546, p-val = 0.8152
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2250 0.0957 -2.3505 0.0188 -0.4126 -0.0374 *
## continentEurope -0.0254 0.1088 -0.2337 0.8152 -0.2388 0.1879
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0329
## I^2 (residual heterogeneity / unaccounted variability): 87.50%
## H^2 (unaccounted variability / sampling variability): 8.00
## R^2 (amount of heterogeneity accounted for): 6.17%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.9969, p-val = 0.0047
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2172, p-val = 0.2699
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0468 0.0448 -1.0455 0.2958 -0.1345 0.0409
## continentEurope 0.0565 0.0512 1.1033 0.2699 -0.0439 0.1568
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7013 -7.4025 -1.4025 -7.4025 22.5975
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0004, p-val = 0.9840
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.5695, p-val = 0.1089
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2841 0.0629 -4.5150 <.0001 -0.4074 -0.1608 ***
## mean.age 0.0020 0.0012 1.6030 0.1089 -0.0004 0.0044
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2774 -2.5548 3.4452 -2.5548 27.4452
##
## tau^2 (estimated amount of residual heterogeneity): 0.0040 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0634
## I^2 (residual heterogeneity / unaccounted variability): 88.40%
## H^2 (unaccounted variability / sampling variability): 8.62
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6193, p-val = 0.0033
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2888, p-val = 0.5910
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4199 0.3273 -1.2828 0.1995 -1.0613 0.2216
## mean.age 0.0032 0.0060 0.5374 0.5910 -0.0085 0.0149
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0423
## I^2 (residual heterogeneity / unaccounted variability): 89.93%
## H^2 (unaccounted variability / sampling variability): 9.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.9305, p-val = 0.0016
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5253, p-val = 0.4686
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1386 0.1995 0.6945 0.4873 -0.2525 0.5296
## mean.age -0.0026 0.0036 -0.7248 0.4686 -0.0096 0.0044
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2405 -4.4809 1.5191 -4.4809 25.5191
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0193
## I^2 (residual heterogeneity / unaccounted variability): 56.40%
## H^2 (unaccounted variability / sampling variability): 2.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2935, p-val = 0.1299
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0098, p-val = 0.9210
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1834 0.0674 -2.7222 0.0065 -0.3155 -0.0514 **
## scale1 0.0007 0.0069 0.0992 0.9210 -0.0129 0.0143
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5124 -3.0248 2.9752 -3.0248 26.9752
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.0260
## I^2 (residual heterogeneity / unaccounted variability): 23.85%
## H^2 (unaccounted variability / sampling variability): 1.31
## R^2 (amount of heterogeneity accounted for): 78.57%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3131, p-val = 0.2518
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.3530, p-val = 0.0369
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0352 0.1075 -0.3271 0.7436 -0.2459 0.1755
## scale1 -0.0231 0.0111 -2.0864 0.0369 -0.0448 -0.0014 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3270 -4.6540 1.3460 -4.6540 25.3460
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0081
## I^2 (residual heterogeneity / unaccounted variability): 11.71%
## H^2 (unaccounted variability / sampling variability): 1.13
## R^2 (amount of heterogeneity accounted for): 94.35%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.1326, p-val = 0.2872
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7750, p-val = 0.0092
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1303 0.0520 2.5082 0.0121 0.0285 0.2321 *
## scale1 -0.0134 0.0052 -2.6029 0.0092 -0.0235 -0.0033 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2994 -6.5987 -2.5987 -5.2124 9.4013
##
## tau^2 (estimated amount of total heterogeneity): 0.0021 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0458
## I^2 (total heterogeneity / total variability): 97.26%
## H^2 (total variability / sampling variability): 36.47
##
## Test for Heterogeneity:
## Q(df = 2) = 80.1038, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3793 0.0269 14.1094 <.0001 0.3266 0.4320 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4544 -2.9089 3.0911 -2.9089 27.0911
##
## tau^2 (estimated amount of residual heterogeneity): 0.0032 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0561
## I^2 (residual heterogeneity / unaccounted variability): 98.69%
## H^2 (unaccounted variability / sampling variability): 76.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 76.6266, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3376, p-val = 0.5612
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4067 0.0574 7.0851 <.0001 0.2942 0.5192 ***
## continentEurope -0.0406 0.0699 -0.5811 0.5612 -0.1777 0.0964
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3561 -2.7121 3.2879 -2.7121 27.2879
##
## tau^2 (estimated amount of residual heterogeneity): 0.0038 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0620
## I^2 (residual heterogeneity / unaccounted variability): 98.74%
## H^2 (unaccounted variability / sampling variability): 79.09
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 79.0924, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1024, p-val = 0.7490
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2966 0.2612 1.1357 0.2561 -0.2153 0.8085
## mean.age 0.0015 0.0046 0.3200 0.7490 -0.0075 0.0105
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7718 -7.5437 -1.5437 -7.5437 22.4563
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0049, p-val = 0.9441
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 80.0989, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1858 0.0227 8.2027 <.0001 0.1414 0.2302 ***
## scale1 0.0200 0.0022 8.9498 <.0001 0.0156 0.0244 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5964 -7.1929 -3.1929 -5.8066 8.8071
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0395
## I^2 (total heterogeneity / total variability): 96.99%
## H^2 (total variability / sampling variability): 33.19
##
## Test for Heterogeneity:
## Q(df = 2) = 95.0453, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0845 0.0234 -3.6120 0.0003 -0.1303 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5029 -3.0057 2.9943 -3.0057 26.9943
##
## tau^2 (estimated amount of residual heterogeneity): 0.0029 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (residual heterogeneity / unaccounted variability): 98.95%
## H^2 (unaccounted variability / sampling variability): 94.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 94.9767, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0765, p-val = 0.7822
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0971 0.0553 -1.7567 0.0790 -0.2055 0.0112 .
## continentEurope 0.0186 0.0671 0.2765 0.7822 -0.1130 0.1501
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4657 -2.9314 3.0686 -2.9314 27.0686
##
## tau^2 (estimated amount of residual heterogeneity): 0.0031 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0.0555
## I^2 (residual heterogeneity / unaccounted variability): 98.73%
## H^2 (unaccounted variability / sampling variability): 78.62
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.6236, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0026, p-val = 0.9595
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0727 0.2353 -0.3089 0.7574 -0.5338 0.3885
## mean.age -0.0002 0.0041 -0.0508 0.9595 -0.0083 0.0079
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8767 -5.7534 0.2466 -5.7534 24.2466
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0092
## I^2 (residual heterogeneity / unaccounted variability): 46.01%
## H^2 (unaccounted variability / sampling variability): 1.85
## R^2 (amount of heterogeneity accounted for): 94.53%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8523, p-val = 0.1735
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 24.4945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0841 0.0351 2.3939 0.0167 0.0152 0.1530 *
## scale1 -0.0178 0.0036 -4.9492 <.0001 -0.0248 -0.0107 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5698 -7.1395 -3.1395 -5.7532 8.8605
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0401
## I^2 (total heterogeneity / total variability): 97.08%
## H^2 (total variability / sampling variability): 34.25
##
## Test for Heterogeneity:
## Q(df = 2) = 98.2054, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0851 0.0237 -3.5915 0.0003 -0.1316 -0.0387 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4880 -2.9760 3.0240 -2.9760 27.0240
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.0544
## I^2 (residual heterogeneity / unaccounted variability): 98.98%
## H^2 (unaccounted variability / sampling variability): 98.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 98.1211, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0743, p-val = 0.7852
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0977 0.0560 -1.7434 0.0813 -0.2076 0.0121 .
## continentEurope 0.0186 0.0681 0.2725 0.7852 -0.1149 0.1520
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4517 -2.9034 3.0966 -2.9034 27.0966
##
## tau^2 (estimated amount of residual heterogeneity): 0.0032 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0563
## I^2 (residual heterogeneity / unaccounted variability): 98.77%
## H^2 (unaccounted variability / sampling variability): 81.17
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 81.1743, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0022, p-val = 0.9623
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0740 0.2385 -0.3102 0.7564 -0.5414 0.3935
## mean.age -0.0002 0.0042 -0.0472 0.9623 -0.0084 0.0080
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8471 -5.6941 0.3059 -5.6941 24.3059
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0099
## I^2 (residual heterogeneity / unaccounted variability): 49.69%
## H^2 (unaccounted variability / sampling variability): 1.99
## R^2 (amount of heterogeneity accounted for): 93.90%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.9878, p-val = 0.1586
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 22.7786, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0852 0.0367 2.3193 0.0204 0.0132 0.1572 *
## scale1 -0.0179 0.0038 -4.7727 <.0001 -0.0253 -0.0106 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6090 -7.2180 -3.2180 -5.8317 8.7820
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (total heterogeneity / total variability): 97.01%
## H^2 (total variability / sampling variability): 33.46
##
## Test for Heterogeneity:
## Q(df = 2) = 93.2724, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0875 0.0232 -3.7678 0.0002 -0.1330 -0.0420 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2033 -6.4066 -2.4066 -5.0203 9.5934
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0450
## I^2 (total heterogeneity / total variability): 84.26%
## H^2 (total variability / sampling variability): 6.35
##
## Test for Heterogeneity:
## Q(df = 2) = 14.1111, p-val = 0.0009
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2506 0.0285 -8.7987 <.0001 -0.3064 -0.1948 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5214 -3.0427 2.9573 -3.0427 26.9573
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0526
## I^2 (residual heterogeneity / unaccounted variability): 98.93%
## H^2 (unaccounted variability / sampling variability): 93.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 93.2694, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1047, p-val = 0.7463
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1020 0.0543 -1.8795 0.0602 -0.2083 0.0044 .
## continentEurope 0.0213 0.0659 0.3236 0.7463 -0.1078 0.1505
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2849 -2.5699 3.4301 -2.5699 27.4301
##
## tau^2 (estimated amount of residual heterogeneity): 0.0042 (SE = 0.0063)
## tau (square root of estimated tau^2 value): 0.0645
## I^2 (residual heterogeneity / unaccounted variability): 92.83%
## H^2 (unaccounted variability / sampling variability): 13.94
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.9408, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0228, p-val = 0.8799
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2583 0.0693 -3.7261 0.0002 -0.3941 -0.1224 ***
## continentEurope 0.0127 0.0839 0.1511 0.8799 -0.1518 0.1771
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4743 -2.9486 3.0514 -2.9486 27.0514
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0550
## I^2 (residual heterogeneity / unaccounted variability): 98.73%
## H^2 (unaccounted variability / sampling variability): 78.84
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.8371, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0089, p-val = 0.9247
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0657 0.2332 -0.2818 0.7781 -0.5228 0.3914
## mean.age -0.0004 0.0041 -0.0945 0.9247 -0.0084 0.0077
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2703 -2.5406 3.4594 -2.5406 27.4594
##
## tau^2 (estimated amount of residual heterogeneity): 0.0042 (SE = 0.0065)
## tau (square root of estimated tau^2 value): 0.0651
## I^2 (residual heterogeneity / unaccounted variability): 91.90%
## H^2 (unaccounted variability / sampling variability): 12.34
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.3430, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0049, p-val = 0.9444
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2692 0.2848 -0.9453 0.3445 -0.8275 0.2890
## mean.age 0.0004 0.0050 0.0697 0.9444 -0.0095 0.0102
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0747 -6.1493 -0.1493 -6.1493 23.8507
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0053
## I^2 (residual heterogeneity / unaccounted variability): 22.53%
## H^2 (unaccounted variability / sampling variability): 1.29
## R^2 (amount of heterogeneity accounted for): 98.17%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.2908, p-val = 0.2559
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 47.4964, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0833 0.0260 3.1984 0.0014 0.0322 0.1343 **
## scale1 -0.0180 0.0026 -6.8918 <.0001 -0.0232 -0.0129 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3448 -4.6897 1.3103 -4.6897 25.3103
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0120
## I^2 (residual heterogeneity / unaccounted variability): 26.64%
## H^2 (unaccounted variability / sampling variability): 1.36
## R^2 (amount of heterogeneity accounted for): 92.92%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3631, p-val = 0.2430
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.6534, p-val = 0.0033
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0461 0.0741 -0.6224 0.5337 -0.1914 0.0991
## scale1 -0.0215 0.0073 -2.9417 0.0033 -0.0358 -0.0072 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis:
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7041 -7.4081 -3.4081 -6.0218 8.5919
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0370
## I^2 (total heterogeneity / total variability): 93.36%
## H^2 (total variability / sampling variability): 15.06
##
## Test for Heterogeneity:
## Q(df = 2) = 45.2773, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0962 0.0227 -4.2407 <.0001 -0.1406 -0.0517 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8449 -5.6899 -1.6899 -4.3036 10.3101
##
## tau^2 (estimated amount of total heterogeneity): 0.0027 (SE = 0.0037)
## tau (square root of estimated tau^2 value): 0.0518
## I^2 (total heterogeneity / total variability): 82.38%
## H^2 (total variability / sampling variability): 5.67
##
## Test for Heterogeneity:
## Q(df = 2) = 13.7562, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2454 0.0349 -7.0363 <.0001 -0.3138 -0.1770 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.8762 -7.7523 -3.7523 -6.3660 8.2477
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0243
## I^2 (total heterogeneity / total variability): 75.61%
## H^2 (total variability / sampling variability): 4.10
##
## Test for Heterogeneity:
## Q(df = 2) = 5.4476, p-val = 0.0656
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0243 0.0167 1.4496 0.1472 -0.0085 0.0570
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5758 -3.1516 2.8484 -3.1516 26.8484
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0035)
## tau (square root of estimated tau^2 value): 0.0495
## I^2 (residual heterogeneity / unaccounted variability): 97.67%
## H^2 (unaccounted variability / sampling variability): 42.87
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 42.8652, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0532, p-val = 0.8175
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0852 0.0540 -1.5786 0.1144 -0.1909 0.0206
## continentEurope -0.0149 0.0645 -0.2307 0.8175 -0.1414 0.1116
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3365 -2.6730 3.3270 -2.6730 27.3270
##
## tau^2 (estimated amount of residual heterogeneity): 0.0037 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0.0609
## I^2 (residual heterogeneity / unaccounted variability): 91.88%
## H^2 (unaccounted variability / sampling variability): 12.31
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.3079, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2818, p-val = 0.5955
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2031 0.0861 -2.3596 0.0183 -0.3718 -0.0344 *
## continentEurope -0.0515 0.0971 -0.5309 0.5955 -0.2418 0.1388
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.6039, p-val = 0.4371
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.8437, p-val = 0.0277
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0276 0.0276 -0.9979 0.3183 -0.0817 0.0266
## continentEurope 0.0623 0.0283 2.2008 0.0277 0.0068 0.1178 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6524 -3.3049 2.6951 -3.3049 26.6951
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0455
## I^2 (residual heterogeneity / unaccounted variability): 96.33%
## H^2 (unaccounted variability / sampling variability): 27.26
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 27.2623, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2391, p-val = 0.6249
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1930 0.2008 -0.9610 0.3366 -0.5867 0.2006
## mean.age 0.0017 0.0036 0.4889 0.6249 -0.0052 0.0087
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5105 -3.0210 2.9790 -3.0210 26.9790
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.0488
## I^2 (residual heterogeneity / unaccounted variability): 83.34%
## H^2 (unaccounted variability / sampling variability): 6.00
## R^2 (amount of heterogeneity accounted for): 11.49%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 6.0027, p-val = 0.0143
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8650, p-val = 0.3523
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4970 0.2718 -1.8286 0.0675 -1.0298 0.0357 .
## mean.age 0.0047 0.0050 0.9301 0.3523 -0.0052 0.0145
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0154
## I^2 (residual heterogeneity / unaccounted variability): 60.24%
## H^2 (unaccounted variability / sampling variability): 2.52
## R^2 (amount of heterogeneity accounted for): 60.01%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5151, p-val = 0.1128
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7751, p-val = 0.0957
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1995 0.1039 1.9209 0.0547 -0.0041 0.4031 .
## mean.age -0.0032 0.0019 -1.6659 0.0957 -0.0071 0.0006 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale:
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9270 -3.8540 2.1460 -3.8540 26.1460
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0315
## I^2 (residual heterogeneity / unaccounted variability): 80.21%
## H^2 (unaccounted variability / sampling variability): 5.05
## R^2 (amount of heterogeneity accounted for): 27.37%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 5.0530, p-val = 0.0246
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.4984, p-val = 0.2209
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0238 0.1003 0.2371 0.8126 -0.1728 0.2203
## scale1 -0.0126 0.0103 -1.2241 0.2209 -0.0327 0.0076
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2869 -2.5738 3.4262 -2.5738 27.4262
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0063)
## tau (square root of estimated tau^2 value): 0.0504
## I^2 (residual heterogeneity / unaccounted variability): 56.95%
## H^2 (unaccounted variability / sampling variability): 2.32
## R^2 (amount of heterogeneity accounted for): 5.40%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.3229, p-val = 0.1275
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7657, p-val = 0.3815
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1002 0.1698 -0.5898 0.5553 -0.4330 0.2327
## scale1 -0.0154 0.0176 -0.8751 0.3815 -0.0498 0.0190
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7430 -3.4860 2.5140 -3.4860 26.5140
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0372
## I^2 (residual heterogeneity / unaccounted variability): 77.37%
## H^2 (unaccounted variability / sampling variability): 4.42
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.4187, p-val = 0.0355
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5311, p-val = 0.4661
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1073 0.1207 0.8891 0.3740 -0.1293 0.3439
## scale1 -0.0090 0.0123 -0.7288 0.4661 -0.0332 0.0152
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5.7. Social risk-taking
Intercept only model
Models results:
Fixed effect model
Models results:
Linear model
Models results:
Plot age trajectory:

Linear with gender model
Models results:
Plot age trajectory:

Linear with gender interaction model
Models results:
Plot age trajectory:
